other comments in the paper if accepted

We appreciate the valuable comments from the reviewers. We will answer reviewers' questions from three aspects, i.e., In respond to Reviewer 5, this paper's major novelty is developing a new STL-based learning framework to Our method creates a practical way to ensure the logic rules' satisfaction in an end-to-end manner. Our approach achieves promising results on real city datasets, i.e., significantly We have carefully compared our work with all the related papers pointed out by the reviewers. Therefore, we also choose STL to express the model properties. Using STL to specify CPS properties is not our novelty.