(Note that I don't have much expertise with this material.)
This seems to be a term they coined themselves in the same paper (my italics):
4.2. Model Generalization
In order to show that our image-based model generalizes
to states that it has not seen before (even states that the
expert has not seen before) we devised the following experiment
on Game 1: Navigate to Object (“Go to Goal0”).
Very surprisingly, we do not see generalization at all. We
actually see the agent taking more-or-less random actions in
the unexplored states. We believe that this occurs not because of a fault in
the GAIL or InfoGAIL algorithms, but because this “Grid
World” environment and expert lend themselves to creating
sharply-peaked state-action distributions. An example
of how this could occur is shown in Fig. 5. Since the agent
learns to match this state-action distribution, it will similarly
always take actions to the right of the world. However, if it
overshoots the goal (due to some noise in the agent’s output
action), the agent will never make it back to the goal since
its learned state-action distribution is completely skewed to
taking actions to the right (to match the expert). Thus, it
will never take actions to the left to correct its overshoot.
We call this problem the “DAgger problem” as it is reminiscent
of a previous RL method  that suffered from the
inability to generalize because it cannot work on states that
an expert has not been to.
 S. Ross, G. J. Gordon, and J. A. Bagnell. No-regret reductions
for imitation learning and structured prediction. CoRR,
I can't seem to find a copy of Ross et al. online, but the description seems clear enough to me. The algorithm learns to match an expert that has never been in a novel situation and the actions it has learned to mimic mindlessly1 are inappropriate for the new situation.
1. This is editorializing on my part, but I think it's relevant here to remind ourselves here that strong AI doesn't exist yet and that's part of the explanation for why this fails in the way that it does.