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: "sparse geometric representation through local shape probing"
  Abstract: We propose a new shape analysis approach based on the non-local analysis of local shape variations. Our method relies on a novel description of shape variations, called Local Probing Field (LPF), which describes how a local probing operator transforms a pattern onto the shape. By carefully optimizing the position and orientation of each descriptor, we are able to capture shape similarities and gather them into a geometrically relevant dictionary over which the shape decomposes sparsely. This new representation permits to handle shapes with mixed intrinsic dimensionality (e.g., shapes containing both surfaces and curves) and to encode various shape features such as boundaries. Our shape representation has several potential applications; here we demonstrate its efficiency for shape resampling and point set denoising for both synthetic and real data.

Connected through hyperedge HE-1 (induced by source paper "pointcleannet learning to denoise and remove outliers from dense point clouds"):
  with members:
    - "p2p net bidirectional point displacement net for shape transform"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "dynamic graph cnn for learning on point clouds"
    - "pu net point cloud upsampling network"
    - "pcpnet learning local shape properties from raw point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-2 (induced by source paper "a minimalist approach to type agnostic detection of quadrics in point clouds"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "grasping unknown objects in clutter by superquadric representation"
    - "cad priors for accurate and flexible instance reconstruction"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "real time 3d scene description using spheres cones and cylinders"
    - "a large dataset of object scans"
    - "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"

Connected through hyperedge HE-3 (induced by source paper "low rank matrix approximation for geometry filtering"):
  with members:
    - "a closed form solution to tensor voting theory and applications"
    - "a general framework for bilateral and mean shift filtering"
    - "tilt transform invariant low rank textures"

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.CG(Computational Geometry)