# Question about SVM Large Margin Intuition

I have a question about support vector machines. I was watching Andrew Ng's lecture on "Large Margin Intuition". Starting from 0:18 of the video, he says the following:

If $$y = 1$$, we want $$\theta^Tx \geq 1$$ (not just $$\geq 0$$)

If $$y = 0$$, we want $$\theta^Tx \leq -1$$ (not just $$< 0$$)

Please see screen shot of video below:

My question is the following: What if we have an observation in which $$-1<\theta^Tx<1$$? How do we classify the observation in that case? Is it $$y = 0$$? Or is it $$y = 1$$? Or is it neither?