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: "graph based denoising for time varying point clouds"
  Abstract: Noisy 3D point clouds arise in many applications. They may be due to errors when creating a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a technique that uses this graph structure and convex optimization methods to denoise 3D point clouds. A short discussion presents how those methods naturally generalize to time-varying inputs such as 3D point cloud time series.

Connected through hyperedge HE-1 (induced by source paper "feature graph learning for 3d point cloud denoising"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graph based blind image deblurring from a single photograph"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-2 (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"
    - "fast resampling of three dimensional point clouds via graphs"
    - "pu net point cloud upsampling network"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"
    - "shapenet an information rich 3d model repository"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-3 (induced by source paper "weighted multi projection 3d point cloud denoising with estimated tangent planes"):
  with members:
    - "fast resampling of three dimensional point clouds via graphs"
    - "shapenet an information rich 3d model repository"

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)