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: "efficient deformable shape correspondence via kernel matching"
  Abstract: We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming. Surprisingly, in spite of the highly non-convex nature of the resulting quadratic assignment problem, our method converges to a semantically meaningful and continuous mapping in most of our experiments, and scales well. We provide preliminary theoretical analysis and several interpretations of the method.

Connected through hyperedge HE-1 (induced by source paper "smooth shells multi scale shape registration with functional maps"):
  with members:
    - "lbs autoencoder self supervised fitting of articulated meshes to point clouds"
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "divergence free shape interpolation and correspondence"
    - "continuous and orientation preserving correspondences via functional maps"
    - "3d coded 3d correspondences by deep deformation"
    - "farm functional automatic registration method for 3d human bodies"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "point wise map recovery and refinement from functional correspondence"

Connected through hyperedge HE-2 (induced by source paper "spectral coarsening of geometric operators"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "emnist an extension of mnist to handwritten letters"
    - "geometric deep learning going beyond euclidean data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "thingi10k a dataset of 10 000 3d printing models"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-3 (induced by source paper "continuous and orientation preserving correspondences via functional maps"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "point wise map recovery and refinement from functional correspondence"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-4 (induced by source paper "reversible harmonic maps between discrete surfaces"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "point wise map recovery and refinement from functional correspondence"
    - "functional correspondence by matrix completion"
    - "coupled quasi harmonic bases"

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)