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: "spectral descriptors for graph matching"
  Abstract: In this paper, we consider the weighted graph matching problem. Recently, approaches to this problem based on spectral methods have gained significant attention. We propose two graph spectral descriptors based on the graph Laplacian, namely a Laplacian family signature (LFS) on nodes, and a pairwise heat kernel distance on edges. We show the stability of both our descriptors under small perturbation of edges and nodes. In addition, we show that our pairwise heat kernel distance is a noise-tolerant approximation of the classical adjacency matrix-based second order compatibility function. These nice properties suggest a descriptor-based matching scheme, for which we set up an integer quadratic problem (IQP) and apply an approximate solver to find a near optimal solution. We have tested our matching method on a set of randomly generated graphs, the widely-used CMU house sequence and a set of real images. These experiments show the superior performance of our selected node signatures and edge descriptors for graph matching, as compared with other existing signature-based matchings and adjacency matrix-based matchings.

Connected through hyperedge HE-1 (induced by source paper "graph matching with anchor nodes a learning approach"):
  with members:
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"

Connected through hyperedge HE-2 (induced by source paper "efficient deformable shape correspondence via kernel matching"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geodesic distance descriptors"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "functional correspondence by matrix completion"
    - "spectral generalized multi dimensional scaling"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-3 (induced by source paper "distances and isomorphism between networks and the stability of network invariants"):
  with members:
    - "the metric space of networks"
    - "hierarchical quasi clustering methods for asymmetric networks"
    - "stable and informative spectral signatures for graph matching"
    - "deltacon a principled massive graph similarity function"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-4 (induced by source paper "stable and informative spectral signatures for graph matching"):
  with members:
    - "graph matching with anchor nodes a learning approach"
    - "a path following algorithm for the graph matching problem"

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)