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 "rotation invariant convolutions for 3d point clouds deep learning"):
  Abstract: Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks being trained with data augmented with rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the convolution. This convolution operator then serves as the basic building block of a neural network that is robust to point clouds under 6DoF transformations such as translation and rotation. Our experiment shows that our method performs with high accuracy in common scene understanding tasks such as object classification and segmentation. Compared to previous works, most importantly, our method is able to generalize and achieve consistent results across different scenarios in which training and testing can contain arbitrary rotations.

Includes members:
  Member "shellnet efficient point cloud convolutional neural networks using concentric shells statistics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "spidercnn deep learning on point sets with parameterized convolutional filters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  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 "dynamic graph cnn for learning on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "tangent convolutions for dense prediction in 3d":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "escape from cells deep kd networks for the recognition of 3d point cloud models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "learning compact geometric features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")

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)