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: "point wise map recovery and refinement from functional correspondence"
  Abstract: Since their introduction in the shape analysis community, functional maps have met with considerable success due to their ability to compactly represent dense correspondences between deformable shapes, with applications ranging from shape matching and image segmentation, to exploration of large shape collections. Despite the numerous advantages of such representation, however, the problem of converting a given functional map back to a point-to-point map has received a surprisingly limited interest. In this paper we analyze the general problem of point-wise map recovery from arbitrary functional maps. In doing so, we rule out many of the assumptions required by the currently established approach -- most notably, the limiting requirement of the input shapes being nearly-isometric. We devise an efficient recovery process based on a simple probabilistic model. Experiments confirm that this approach achieves remarkable accuracy improvements in very challenging cases.

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"
    - "efficient deformable shape correspondence via kernel matching"

Connected through hyperedge HE-2 (induced by source paper "zoomout spectral upsampling for efficient shape correspondence"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "unsupervised deep learning for structured shape matching"
    - "fast and accurate intrinsic symmetry detection"
    - "farm functional automatic registration method for 3d human bodies"
    - "reversible harmonic maps between discrete surfaces"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "localized manifold harmonics for spectral shape analysis"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-3 (induced by source paper "shape analysis via functional map construction and bases pursuit"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "robust structure based shape correspondence"
    - "functional correspondence by matrix completion"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-4 (induced by source paper "unsupervised deep learning for structured shape matching"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "partial functional correspondence"

Connected through hyperedge HE-5 (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"
    - "efficient deformable shape correspondence via kernel matching"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-6 (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"
    - "efficient deformable shape correspondence via kernel matching"
    - "functional correspondence by matrix completion"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-7 (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"):
  with members:
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "spectrometer amortized sublinear spectral approximation of distance on graphs"
    - "on the optimality of shape and data representation in the spectral domain"
    - "functional correspondence by matrix completion"
    - "matching lbo eigenspace of non rigid shapes via high order statistics"
    - "spectral generalized multi dimensional scaling"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "spectrometer amortized sublinear spectral approximation of distance on graphs"):
  with members:
    - "bayesian inference of bijective non rigid shape correspondence"
    - "on the optimality of shape and data representation in the spectral domain"
    - "geodesics in heat a new approach to computing distance based on heat flow"
    - "spectral generalized multi dimensional scaling"
    - "a correspondence less approach to matching of deformable shapes"
    - "a tutorial on spectral clustering"

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)