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: "stable and informative spectral signatures for graph matching"
  Abstract: In this paper, we consider the approximate weighted graph matching problem and introduce stable and informative first and second order compatibility terms suitable for inclusion into the popular integer quadratic program formulation. Our approach relies on a rigorous analysis of stability of spectral signatures based on the graph Laplacian. In the case of the first order term, we derive an objective function that measures both the stability and informativeness of a given spectral signature. By optimizing this objective, we design new spectral node signatures tuned to a specific graph to be matched. We also introduce the pairwise heat kernel distance as a stable second order compatibility term; we justify its plausibility by showing that in a certain limiting case it converges to the classical adjacency matrix-based second order compatibility function. We have tested our approach on a set of synthetic graphs, the widely-used CMU house sequence, and a set of real images. These experiments show the superior performance of our first and second order compatibility terms as compared with the commonly used ones.

Connected through hyperedge HE-1 (induced by source paper "kergm kernelized graph matching"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "triangular alignment tame a tensor based approach for higher order network alignment"
    - "subgraph matching kernels for attributed graphs"
    - "a path following algorithm for the graph matching problem"

Connected through hyperedge HE-2 (induced by source paper "atol automatic topologically oriented learning"):
  with members:
    - "deep sets"
    - "sliced wasserstein kernel for persistence diagrams"
    - "deep learning with topological signatures"

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"
    - "spectral descriptors 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 "distributable consistent multi object matching"):
  with members:
    - "graph matching with anchor nodes a learning approach"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "distributed consistent data association"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-5 (induced by source paper "graph based compression of dynamic 3d point cloud sequences"):
  with members:
    - "learning parametric dictionaries for signals on graphs"
    - "algorithm 937 minres qlp for symmetric and hermitian linear equations and least squares problems"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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)