human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "viewsynth learning local features from depth using view synthesis"):
  Abstract: We address the problem of jointly detecting keypoints and learning descriptors in depth data with challenging viewpoint changes. Despite great improvements in recent RGB based local feature learning methods, we show that these methods cannot be directly transferred to the depth image modality. These methods also do not utilize the 2.5D information present in depth images. We propose a framework ViewSynth, designed to jointly learn 3D structure aware depth image representation, and local features from that representation. ViewSynth consists of `View Synthesis Network' (VSN), trained to synthesize depth image views given a depth image representation and query viewpoints. ViewSynth framework includes joint learning of keypoints and feature descriptor, paired with our view synthesis loss, which guides the model to propose keypoints robust to viewpoint changes. We demonstrate the effectiveness of our formulation on several depth image datasets, where learned local features using our proposed ViewSynth framework outperforms the state-of-the-art methods in keypoint matching and camera localization tasks.

Includes members:
  Member "d2 net a trainable cnn for joint detection and description of local features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "deeply supervised depth map super resolution as novel view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pag net progressive attention guided depth super resolution network")
  Member "r2d2 repeatable and reliable detector and descriptor":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "discovery of latent 3d keypoints via end to end geometric reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "learning local rgb to cad correspondences for object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for 2d and 3d rotatable data an overview of methods")
  Member "end to end learning of keypoint detector and descriptor for pose invariant 3d matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation")
  Member "extreme view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeing the world in a bag of chips")
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)