human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"):
  Abstract: Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space. Such are, for example, various point-wise correspondence recovery algorithms used as a post-processing stage in the functional correspondence framework. Such frequently used techniques implicitly make restrictive assumptions (e.g., near-isometry) on the considered shapes and in practice suffer from lack of accuracy and result in poor surjectivity. We propose an alternative recovery technique capable of guaranteeing a bijective correspondence and producing significantly higher accuracy and smoothness. Unlike other methods our approach does not depend on the assumption that the analyzed shapes are isometric. We derive the proposed method from the statistical framework of kernel density estimation and demonstrate its performance on several challenging deformable 3D shape matching datasets.

Includes members:
  Member "efficient globally optimal 2d to 3d deformable shape matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "spectrometer amortized sublinear spectral approximation of distance on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a minimalistic approach for fast computation of geodesic distances on triangular meshes")
  Member "point wise map recovery and refinement from functional correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "smooth shells multi scale shape registration with functional maps")
  Member "on the optimality of shape and data representation in the spectral domain":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilateral operators for functional maps")
  Member "functional correspondence by matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep geometric matrix completion are we doing it right")
  Member "matching lbo eigenspace of non rigid shapes via high order statistics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian inference of bijective non rigid shape correspondence")
  Member "spectral generalized multi dimensional scaling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment")
  Member "sparse modeling of intrinsic correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graph domain adaptation with localized graph signal representations")

Question: Please tell me which class the source paper 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)