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 "spatial transformer for 3d point clouds"):
  Abstract: Deep neural networks can efficiently process the 3D point cloud data. At each layer, the network needs to partition points into multiple local patches, and then learn features from them, in order to understand the geometric information encoded in the 3D point cloud.Previous networks adopt all the same local patches for different layers, as they utilized the same fixed original 3D point coordinates to define local neighborhoods. It is easy to implement but not necessarily optimal. Ideally local patches should be different at different layers so as to adapt to the specific layer for efficient feature learning. One way to achieve this is to learn different transformations of the original point cloud at each layer, and then learn features from local patches defined on transformed coordinates. In this work, we propose a novel approach to learn different non-rigid transformations of the input point cloud for different local neighborhoods at each layer. We propose both linear (affine) and non-linear (projective and deformable) spatial transformer for 3D points. With spatial transformers on theShapeNet part segmentation dataset, the network achieves higher accuracy for all categories, specifically with 8% gain on earphones and rockets. The proposed methods also outperform the state-of-the-art methods in several other point cloud processing tasks(classification, semantic segmentation and detection). Visualizations show that spatial transformers can learn features more efficiently by altering local neighborhoods according to the semantic information of 3D shapes regardless of variations in a category.

Includes members:
  Member "multiresolution tree networks for 3d point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "splatnet sparse lattice networks for point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "stereo vision based semantic 3d object and ego motion tracking for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "so net self organizing network for point cloud analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "learning and matching multi view descriptors for registration of point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")
  Member "fully convolutional point networks for large scale point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bi directional attention for joint instance and semantic segmentation in point clouds")
  Member "dynamic graph cnn for learning on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "escape from cells deep kd networks for the recognition of 3d point cloud models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)