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 "geometric back projection network for point cloud classification"):
  Abstract: As the basic task of point cloud learning, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a CNN based network leveraging an idea of error-correcting feedback structure to comprehensively capture the local features of 3D point clouds. Besides, we also enrich the explicit and implicit geometric information of point clouds in low-level 3D space and high-level feature space, respectively. By applying an attention module based on channel affinity, that focuses on distinct channels, the learned feature map of our network can effectively avoid redundancy. The performance on synthetic and real-world datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency.

Includes members:
  Member "hierarchical back projection network for image super resolution":
    also connected through other hyperedges:
      (none)
  Member "pyramnet point cloud pyramid attention network and graph embedding module for classification and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometric feedback network for point cloud classification")
  Member "point attention network for semantic segmentation of 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pointwise attention based atrous convolutional neural networks")
  Member "pcan 3d attention map learning using contextual information for point cloud based retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation")
  Member "densepoint learning densely contextual representation for efficient point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")
  Member "kpconv flexible and deformable convolution for point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "real image denoising with feature attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)