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: "matching lbo eigenspace of non rigid shapes via high order statistics"
  Abstract: A fundamental tool in shape analysis is the virtual embedding of the Riemannian manifold describing the geometry of a shape into Euclidean space. Several methods have been proposed to embed isometric shapes in flat domains while preserving distances measured on the manifold. Recently, attention has been given to embedding shapes into the eigenspace of the Lapalce-Beltrami operator. The Laplace-Beltrami eigenspace preserves the diffusion distance, and is invariant under isometric transformations. However, Laplace-Beltrami eigenfunctions computed independently for different shapes are often incompatible with each other. Applications involving multiple shapes, such as pointwise correspondence, would greatly benefit if their respective eigenfunctions were somehow matched. Here, we introduce a statistical approach for matching eigenfunctions. We consider the values of the eigenfunctions over the manifold as sampling of random variables, and try to match their multivariate distributions. Comparing distributions is done indirectly, using high order statistics. We show that the permutation and sign ambiguities of low order eigenfunctions, can be inferred by minimizing the difference of their third order moments. The sign ambiguities of antisymmetric eigenfunctions can be resolved by exploiting isometric invariant relations between the gradients of the eigenfunctions and the surface normal. We present experiments demonstrating the success of the proposed method applied to feature point correspondence.

Connected through hyperedge HE-1 (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"
    - "point wise map recovery and refinement from functional correspondence"
    - "on the optimality of shape and data representation in the spectral domain"
    - "functional correspondence by matrix completion"
    - "spectral generalized multi dimensional scaling"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-2 (induced by source paper "bayesian inference of bijective non rigid shape correspondence"):
  with members:
    - "functional correspondence by matrix completion"
    - "geodesics in heat a new approach to computing distance based on heat flow"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-3 (induced by source paper "functional correspondence by matrix completion"):
  with members:
    - "matrix completion on graphs"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "coupled quasi harmonic bases"
    - "multimodal diffusion geometry by joint diagonalization of laplacians"
    - "sparse modeling of intrinsic correspondences"
    - "smi 2011 full paper diffusion geometric maximally stable component detection in deformable shapes"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "on nonrigid shape similarity and correspondence"):
  with members:
    - "coupled quasi harmonic bases"
    - "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)