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 "functional correspondence by matrix completion"):
  Abstract: In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional correspondence problem as matrix completion with manifold geometric structure and inducing functional localization with the $L_1$ norm. We discuss efficient numerical procedures for the solution of our problem. Our method compares favorably to the accuracy of state-of-the-art correspondence algorithms on non-rigid shape matching benchmarks, and is especially advantageous in settings when only scarce data is available.

Includes members:
  Member "matrix completion on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "context aware visual compatibility prediction")
  Member "matching lbo eigenspace of non rigid shapes via high order statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space")
  Member "manopt a matlab toolbox for optimization on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint constellation design for the two user non coherent multiple access channel")
  Member "coupled quasi harmonic bases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "operatornet recovering 3d shapes from difference operators")
  Member "multimodal diffusion geometry by joint diagonalization of laplacians":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal image retrieval with random walk on multi layer graphs")
  Member "sparse modeling of intrinsic correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph domain adaptation with localized graph signal representations")
  Member "smi 2011 full paper diffusion geometric maximally stable component detection in deformable shapes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self functional maps")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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.CV(Computer Vision and Pattern Recognition)