To bring things full circle: the cross-entropy loss is the KL divergence. So intuitively, when you're minimizing cross-entropy loss, you're trying to minimize the "divergence" between the true distribution and your model distribution.
This intuition really helped me understand CE loss.
This intuition really helped me understand CE loss.