What does an edge mean during a variable split in Random Forest?

I am currently writing a blog where I plan to do a regression model using Random Forest. Random Forest averages the estimated prediction from many decision trees that are fitted on to a bootstrap from the dataset while choosing K<=M variables randomly from all the M dependent variables.

However, I recently came across this presentation. One of the slides says something like this:

Here, the author is building a regression model using Random Forest with Residual Sum of Squares as the splitting criterion. During the splitting process, it is stated that a split is "too close to the edge."

My question is, what does an "edge" mean in this case? And why not choose a split if it is too close to the "edge"?

After going through the linked slides, I think what Cutler tries to say is that if you were to split at $$3.07$$, you would be rather close to the minimum value of that variable in the sample. Hence, a split near the 'edge' would yield one node containing almost all of the (remaining) observations and the other containing only one or a few. To avoid this imbalance, he suggests splitting slightly further away from the extremes.