human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "pyramnet point cloud pyramid attention network and graph embedding module for classification and segmentation"
  Abstract: With the tide of artificial intelligence, we try to apply deep learning to understand 3D data. Point cloud is an important 3D data structure, which can accurately and directly reflect the real world. In this paper, we propose a simple and effective network, which is named PyramNet, suites for point cloud object classification and semantic segmentation in 3D scene. We design two new operators: Graph Embedding Module(GEM) and Pyramid Attention Network(PAN). Specifically, GEM projects point cloud onto the graph and practices the covariance matrix to explore the relationship between points, so as to improve the local feature expression ability of the model. PAN assigns some strong semantic features to each point to retain fine geometric features as much as possible. Furthermore, we provide extensive evaluation and analysis for the effectiveness of PyramNet. Empirically, we evaluate our model on ModelNet40, ShapeNet and S3DIS.

Connected through hyperedge HE-1 (induced by source paper "geometric back projection network for point cloud classification"):
  with members:
    - "hierarchical back projection network for image super resolution"
    - "point attention network for semantic segmentation of 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "kpconv flexible and deformable convolution for point clouds"
    - "real image denoising with feature attention"
    - "pu gan a point cloud upsampling adversarial network"

Connected through hyperedge HE-2 (induced by source paper "geometric feedback network for point cloud classification"):
  with members:
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "kpconv flexible and deformable convolution for point clouds"
    - "real image denoising with feature attention"
    - "gapnet graph attention based point neural network for exploiting local feature of point cloud"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"

Question: Please tell me which class the center node 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)