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: "mining point cloud local structures by kernel correlation and graph pooling"
  Abstract: Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with a more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a set of learnable 3D points that jointly respond to a set of neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly achieve better performances on major datasets. Our code is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

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 "a rotation invariant framework for deep point cloud analysis"):
  with members:
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "total denoising unsupervised learning of 3d point cloud cleaning"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "pu gan a point cloud upsampling adversarial network"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "pointnetlk robust efficient point cloud registration using pointnet"

Connected through hyperedge HE-5 (induced by source paper "lrc net learning discriminative features on point clouds by encodinglocal region contexts"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "point2spatialcapsule aggregating features and spatial relationships of local regions on point clouds using spatial aware capsules"

Connected through hyperedge HE-6 (induced by source paper "pointaugment an auto augmentation framework for point cloud classification"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "multi level semantic feature augmentation for one shot learning"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "efficient learning on point clouds with basis point sets"
    - "kpconv flexible and deformable convolution for point clouds"
    - "fast autoaugment"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"

Connected through hyperedge HE-7 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "flat2layout flat representation for estimating layout of general room types"
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "deep hough voting for 3d object detection in point clouds"
    - "deepperimeter indoor boundary estimation from posed monocular sequences"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"

Connected through hyperedge HE-8 (induced by source paper "irregular convolutional auto encoder on point clouds"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "cgans with projection discriminator"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "local spectral graph convolution for point set feature learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"

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)