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: "fast resampling of three dimensional point clouds via graphs"
  Abstract: To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we propose a randomized resampling strategy that selects a representative subset of points while preserving application-dependent features. The strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling; by minimizing the error, we obtain a general form of optimal resampling distribution. The proposed resampling distribution is guaranteed to be shift-, rotation- and scale-invariant in the three-dimensional space. We then specify the feature-extraction operator to be a graph filter and study specific resampling strategies based on all-pass, low-pass, high-pass graph filtering and graph filter banks. We validate the proposed methods on three applications: Large-scale visualization, accurate registration, and robust shape modeling demonstrating the effectiveness and efficiency of the proposed resampling methods.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

Connected through hyperedge HE-2 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "a graph cnn for 3d point cloud classification"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-3 (induced by source paper "symbiotic graph neural networks for 3d skeleton based human action recognition and motion prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "a neural temporal model for human motion prediction"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "convolutional sequence to sequence model for human dynamics"
    - "look into person joint body parsing pose estimation network and a new benchmark"

Connected through hyperedge HE-4 (induced by source paper "actional structural graph convolutional networks for skeleton based action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "the kinetics human action video dataset"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-5 (induced by source paper "3d point cloud denoising via deep neural network based local surface estimation"):
  with members:
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "pu net point cloud upsampling network"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"
    - "shapenet an information rich 3d model repository"
    - "graph based denoising for time varying point clouds"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "from rank estimation to rank approximation rank residual constraint for image denoising"
    - "group sparsity residual with non local samples for image denoising"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-7 (induced by source paper "weighted multi projection 3d point cloud denoising with estimated tangent planes"):
  with members:
    - "shapenet an information rich 3d model repository"
    - "graph based denoising for time varying point clouds"

Connected through hyperedge HE-8 (induced by source paper "feature preserving and uniformity controllable point cloud simplification on graph"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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)