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: "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.

Connected through hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment"):
  with members:
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "continuous and orientation preserving correspondences via functional maps"
    - "unsupervised deep learning for structured shape matching"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "alternating direction graph matching"

Connected through hyperedge HE-2 (induced by source paper "harmonic alignment"):
  with members:
    - "magan aligning biological manifolds"
    - "geometry based data generation"
    - "heterogeneous multireference alignment a single pass approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-3 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment"):
  with members:
    - "self supervised learning of dense shape correspondence"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "graph matching relax or not"
    - "spectral generalized multi dimensional scaling"
    - "discrete laplace beltrami operator determines discrete riemannian metric"

Connected through hyperedge HE-4 (induced by source paper "fine grained object semantic understanding from correspondences"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "canonical surface mapping via geometric cycle consistency"
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "self supervised learning of dense shape correspondence"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unsupervised deep learning for structured shape matching"

Connected through hyperedge HE-5 (induced by source paper "cyclic functional mapping self supervised correspondence between non isometric deformable shapes"):
  with members:
    - "lbs autoencoder self supervised fitting of articulated meshes to point clouds"
    - "3d coded 3d correspondences by deep deformation"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "partial functional correspondence"

Connected through hyperedge HE-6 (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"
    - "efficient deformable shape correspondence via kernel matching"
    - "point wise map recovery and refinement from functional correspondence"

Connected through hyperedge HE-7 (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"
    - "localized manifold harmonics for spectral shape analysis"
    - "point wise map recovery and refinement from functional correspondence"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-8 (induced by source paper "lifting vectorial variational problems a natural formulation based on geometric measure theory and discrete exterior calculus"):
  with members:
    - "functional maps representation on product manifolds"
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "sublabel accurate discretization of nonconvex free discontinuity problems"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "complexity of discrete energy minimization problems"
    - "sublabel accurate relaxation of nonconvex energies"

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)