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 rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
  Abstract: Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point. Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. We show that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide theoretical analysis for the rotation-invariance achieved by our methods.

Connected through hyperedge HE-1 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "collaborative deep reinforcement learning for joint object search"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-2 (induced by source paper "keypointnet a large scale 3d keypoint dataset aggregated from numerous human annotations"):
  with members:
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "relation shape convolutional neural network for point cloud analysis"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "learning compact geometric features"

Connected through hyperedge HE-3 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical cnns on unstructured grids"
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning an effective equivariant 3d descriptor without supervision"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "equivariant neural networks and equivarification"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-4 (induced by source paper "3d rotation equivariant quaternion neural networks"):
  with members:
    - "complex valued neural networks for privacy protection"
    - "quaternion equivariant capsule networks for 3d point clouds"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "relation shape convolutional neural network for point cloud analysis"
    - "splatnet sparse lattice networks for point cloud processing"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "effective rotation invariant point cnn with spherical harmonics kernels"):
  with members:
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "pcpnet learning local shape properties from raw point clouds"

Connected through hyperedge HE-6 (induced by source paper "srinet learning strictly rotation invariant representations for point cloud classification and segmentation"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "p2p net bidirectional point displacement net for shape transform"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "dynamic graph cnn for learning on point clouds"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mining point cloud local structures by kernel correlation and graph pooling"

Connected through hyperedge HE-7 (induced by source paper "learning an effective equivariant 3d descriptor without supervision"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "sampling matters in deep embedding learning"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "learning compact geometric features"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet point cloud auto encoder via deep grid deformation"

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)