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 "pu gcn point cloud upsampling using graph convolutional networks"):
  Abstract: Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3 novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs) to better encode local point information. Our upsampling modules are versatile and can be incorporated into any point cloud upsampling pipeline. We show how our 3 modules consistently improve state-of-the-art methods in all point upsampling metrics. We also propose a new multi-scale point feature extractor, called Inception DenseGCN. We modify current Inception GCN algorithms by introducing DenseGCN blocks. By aggregating data at multiple scales, our new feature extractor is more resilient to density changes along point cloud surfaces. We combine Inception DenseGCN with one of our upsampling modules (NodeShuffle) into a new point upsampling pipeline: PU-GCN. We show both qualitatively and quantitatively the advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity. The website and source code of this work is available at this https URL and this https URL respectively.

Includes members:
  Member "kpconv flexible and deformable convolution for point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "self attention graph pooling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "pu gan a point cloud upsampling adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "deepgcns can gcns go as deep as cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "deepgcns making gcns go as deep as cnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sgas sequential greedy architecture search")
  Member "hierarchical graph representation learning with differentiable pooling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory based graph networks")
  Member "spidercnn deep learning on point sets with parameterized convolutional filters":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph 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)