human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "ds a flexible scalable and provably tight relaxation for matching problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "hamiltonian operator for spectral shape analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment")
  Member "learning shape correspondence with anisotropic convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "on the optimality of shape and data representation in the spectral domain":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilateral operators for functional maps")
  Member "functional correspondence by matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep geometric matrix completion are we doing it right")
  Member "coupled quasi harmonic bases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "operatornet recovering 3d shapes from difference operators")
  Member "sparse modeling of intrinsic correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph domain adaptation with localized graph signal representations")
  Member "point set registration coherent point drift":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d point cloud processing and learning for autonomous driving")

Question: Please tell me which class the source paper 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)