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: "octree guided cnn with spherical kernels for 3d point clouds"
  Abstract: We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds. The network architecture capitalizes on the sparse nature of irregular point clouds, and hierarchically coarsens the data representation with space partitioning. At the same time, the proposed spherical kernels systematically quantize point neighborhoods to identify local geometric structures in the data, while maintaining the properties of translation-invariance and asymmetry. We specify spherical kernels with the help of network neurons that in turn are associated with spatial locations. We exploit this association to avert dynamic kernel generation during network training that enables efficient learning with high resolution point clouds. The effectiveness of the proposed technique is established on the benchmark tasks of 3D object classification and segmentation, achieving new state-of-the-art on ShapeNet and RueMonge2014 datasets.

Connected through hyperedge HE-1 (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"
    - "kpconv flexible and deformable convolution for point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "sceneencoder scene aware semantic segmentation of point clouds with a learnable scene descriptor"):
  with members:
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "spherical kernel for efficient graph convolution on 3d point clouds"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "dense 3d face correspondence"
    - "learning to sample"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-4 (induced by source paper "randla net efficient semantic segmentation of large scale point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "concrete autoencoders for differentiable feature selection and reconstruction"

Connected through hyperedge HE-5 (induced by source paper "point attention network for semantic segmentation of 3d point clouds"):
  with members:
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "sbnet sparse blocks network for fast inference"
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-6 (induced by source paper "point2spatialcapsule aggregating features and spatial relationships of local regions on point clouds using spatial aware capsules"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "associatively segmenting instances and semantics in point clouds"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "3d point capsule networks"

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)