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: "multimodal diffusion geometry by joint diagonalization of laplacians"
  Abstract: We construct an extension of diffusion geometry to multiple modalities through joint approximate diagonalization of Laplacian matrices. This naturally extends classical data analysis tools based on spectral geometry, such as diffusion maps and spectral clustering. We provide several synthetic and real examples of manifold learning, retrieval, and clustering demonstrating that the joint diffusion geometry frequently better captures the inherent structure of multi-modal data. We also show that many previous attempts to construct multimodal spectral clustering can be seen as particular cases of joint approximate diagonalization of the Laplacians.

Connected through hyperedge HE-1 (induced by source paper "multi modal image retrieval with random walk on multi layer graphs"):
  with members:
    - "clustering with multi layer graphs a spectral perspective"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "functional correspondence by matrix completion"):
  with members:
    - "matrix completion on graphs"
    - "matching lbo eigenspace of non rigid shapes via high order statistics"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "coupled quasi harmonic bases"
    - "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-3 (induced by source paper "making laplacians commute"):
  with members:
    - "almost commuting matrices are almost jointly diagonalizable"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "coupled quasi harmonic bases"
    - "sparse modeling of intrinsic correspondences"
    - "learning multi modal similarity"

Connected through hyperedge HE-4 (induced by source paper "heat kernel coupling for multiple graph analysis"):
  with members:
    - "making laplacians commute"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "coupled quasi harmonic bases"
    - "multimodal similarity preserving hashing"
    - "learning multi modal similarity"

Connected through hyperedge HE-5 (induced by source paper "clustering on multi layer graphs via subspace analysis on grassmann manifolds"):
  with members:
    - "clustering with multi layer graphs a spectral perspective"
    - "learning multi modal similarity"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "structure preserving color transformations using laplacian commutativity"):
  with members:
    - "making laplacians commute"
    - "coupled quasi harmonic bases"

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.CV(Computer Vision and Pattern Recognition)