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: "localized manifold harmonics for spectral shape analysis"
  Abstract: The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer graphics and geometry processing applications. In particular, Laplacian eigenbases allow generalizing the classical Fourier analysis to manifolds. A key drawback of such bases is their inherently global nature, as the Laplacian eigenfunctions carry geometric and topological structure of the entire manifold. In this paper, we introduce a new framework for local spectral shape analysis. We show how to efficiently construct localized orthogonal bases by solving an optimization problem that in turn can be posed as the eigendecomposition of a new operator obtained by a modification of the standard Laplacian. We study the theoretical and computational aspects of the proposed framework and showcase our new construction on the classical problems of shape approximation and correspondence. We obtain significant improvement compared to classical Laplacian eigenbases as well as other alternatives for constructing localized bases.

Connected through hyperedge HE-1 (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"
    - "point wise map recovery and refinement from functional correspondence"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-2 (induced by source paper "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"):
  with members:
    - "functional maps representation on product manifolds"
    - "isospectralization or how to hear shape style and correspondence"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "functional maps representation on product manifolds"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "hamiltonian operator for spectral shape analysis"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "geodesic distance descriptors"
    - "functional correspondence by matrix completion"

Connected through hyperedge HE-4 (induced by source paper "farm functional automatic registration method for 3d human bodies"):
  with members:
    - "shape retrieval of non rigid 3d human models"
    - "multilevel active registration for kinect human body scans from low quality to high quality"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "building statistical shape spaces for 3d human modeling"
    - "deformable shape completion with graph convolutional autoencoders"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "sparse modeling of intrinsic correspondences"

Connected through hyperedge HE-5 (induced by source paper "hamiltonian operator for spectral shape analysis"):
  with members:
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "consistent discretization and minimization of the l1 norm on manifolds"
    - "dense human body correspondences using convolutional networks"
    - "on the optimality of shape and data representation in the spectral domain"
    - "spectral generalized multi dimensional scaling"
    - "schrodinger diffusion for shape analysis with texture"
    - "sparse modeling of intrinsic correspondences"

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)