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: "point wise convolutional neural network"
  Abstract: Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this technical report, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is point-wise convolution, a convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.

Connected through hyperedge HE-1 (induced by source paper "rotation invariant convolutions for 3d point clouds deep learning"):
  with members:
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "learning compact geometric features"

Connected through hyperedge HE-2 (induced by source paper "geometry sharing network for 3d point cloud classification and segmentation"):
  with members:
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "multiresolution tree networks for 3d point cloud processing"
    - "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"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-3 (induced by source paper "normalnet learning based guided normal filtering for mesh denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "mesh based autoencoders for localized deformation component analysis"

Connected through hyperedge HE-4 (induced by source paper "normalnet learning based normal filtering for mesh denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "mesh based autoencoders for localized deformation component analysis"

Connected through hyperedge HE-5 (induced by source paper "deep autoencoder based lossy geometry compression for point clouds"):
  with members:
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "learning representations and generative models for 3d point clouds"
    - "dynamic polygon cloud compression"
    - "end to end optimization of nonlinear transform codes for perceptual quality"

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)