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: "pointwise convolutional neural networks"
  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 paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is pointwise convolution, a new convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.

Connected through hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"

Connected through hyperedge HE-2 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-3 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "relation shape convolutional neural network for point cloud analysis"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-4 (induced by source paper "fpconv learning local flattening for point convolution"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "deepgcns can gcns go as deep as cnns"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"

Connected through hyperedge HE-5 (induced by source paper "bi directional attention for joint instance and semantic segmentation in point clouds"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "jsnet joint instance and semantic segmentation of 3d point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-6 (induced by source paper "3d mininet learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation"):
  with members:
    - "sloam semantic lidar odometry and mapping for forest inventory"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "lu net an efficient network for 3d lidar point cloud semantic segmentation based on end to end learned 3d features and u net"
    - "randla net efficient semantic segmentation of large scale point clouds"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (induced by source paper "autonomous unknown application filtering and labeling for dl based traffic classifier update"):
  with members:
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"

Connected through hyperedge HE-8 (induced by source paper "unsupervised multi task feature learning on point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "3d point capsule networks"
    - "clustering with deep learning taxonomy and new methods"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep clustering for unsupervised learning of visual features"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"

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)