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: "functional maps representation on product manifolds"
  Abstract: We consider the tasks of representing, analyzing and manipulating maps between shapes. We model maps as densities over the product manifold of the input shapes; these densities can be treated as scalar functions and therefore are manipulable using the language of signal processing on manifolds. Being a manifold itself, the product space endows the set of maps with a geometry of its own, which we exploit to define map operations in the spectral domain; we also derive relationships with other existing representations (soft maps and functional maps). To apply these ideas in practice, we discretize product manifolds and their Laplace--Beltrami operators, and we introduce localized spectral analysis of the product manifold as a novel tool for map processing. Our framework applies to maps defined between and across 2D and 3D shapes without requiring special adjustment, and it can be implemented efficiently with simple operations on sparse matrices.

Connected through hyperedge HE-1 (induced by source paper "learning based real time detection of intrinsic reflectional symmetry"):
  with members:
    - "fast and accurate intrinsic symmetry detection"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "adam a method for stochastic optimization"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-2 (induced by source paper "lifting vectorial variational problems a natural formulation based on geometric measure theory and discrete exterior calculus"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"):
  with members:
    - "isospectralization or how to hear shape style and correspondence"
    - "localized manifold harmonics for spectral shape analysis"
    - "hamiltonian operator for spectral shape analysis"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "adam a method for stochastic optimization"
    - "a low dimensional representation for robust partial isometric correspondences computation"

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.GR(Graphics)