If you read the paper itself[1] the random forest is only used in the final stage. The main approach is a convolutional variational autoencoder (which of course is a deep learning model).
Well the humans are doing classification too so I'm not sure what this is supposed to mean.
It's true that this model doesn't know anything outside its training dataset, but that's a different objection.
It's an interesting question as to if there is anything humans do that isn't just classification or regression (and of course regression is just classification over an infinite set where we assign labels to certain ranges and then select the highest probability density).