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 "pointwise attention based atrous convolutional neural networks"):
  Abstract: With the rapid progress of deep convolutional neural networks, in almost all robotic applications, the availability of 3D point clouds improves the accuracy of 3D semantic segmentation methods. Rendering of these irregular, unstructured, and unordered 3D points to 2D images from multiple viewpoints imposes some issues such as loss of information due to 3D to 2D projection, discretizing artifacts, and high computational costs. To efficiently deal with a large number of points and incorporate more context of each point, a pointwise attention-based atrous convolutional neural network architecture is proposed. It focuses on salient 3D feature points among all feature maps while considering outstanding contextual information via spatial channel-wise attention modules. The proposed model has been evaluated on the two most important 3D point cloud datasets for the 3D semantic segmentation task. It achieves a reasonable performance compared to state-of-the-art models in terms of accuracy, with a much smaller number of parameters.

Includes members:
  Member "point attention network for semantic segmentation of 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometric back projection network for point cloud classification")
  Member "interpolated convolutional networks for 3d point cloud understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "deformable filter convolution for point cloud reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deformable kernels adapting effective receptive fields for object deformation")
  Member "kpconv flexible and deformable convolution for point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "dilated point convolutions on the receptive field of point convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language")
  Member "randla net efficient semantic segmentation of large scale point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d mininet learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation")
  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 "tangent convolutions for dense prediction in 3d":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")

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)