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 "3d point capsule networks"):
  Abstract: In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.

Includes members:
  Member "indoor scene understanding in 2 5 3d for autonomous agents a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "monte carlo convolution for learning on non uniformly sampled point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "generalized capsule networks with trainable routing procedure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks")
  Member "videocapsulenet a simplified network for action detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "examining the benefits of capsule neural networks")
  Member "cappronet deep feature learning via orthogonal projections onto capsule subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "subspace capsule network")
  Member "fast capsnet for lung cancer screening":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "examining the benefits of capsule neural networks")
  Member "group equivariant capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attentive group equivariant convolutional networks")

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)