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: "graph matching with anchor nodes a learning approach"
  Abstract: In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes. Our construction exploits recently introduced node signatures based on graph Laplacians, namely the Laplacian family signature (LFS) on the nodes, and the pairwise heat kernel map on the edges. In this paper, without assuming an explicit form of parametric dependence nor a distance metric between node signatures, we formulate an optimization problem which incorporates the knowledge of anchor nodes. Solving this problem gives us an optimized proximity measure specific to the graphs under consideration. Using this as a first order compatibility term, we then set up an integer quadratic program (IQP) to solve for a near optimal graph matching. Our experiments demonstrate the superior performance of our approach on randomly generated graphs and on two widely-used image sequences, when compared with other existing signature and adjacency matrix based graph matching methods.

Connected through hyperedge HE-1 (induced by source paper "distributable consistent multi object matching"):
  with members:
    - "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"
    - "stable and informative spectral signatures for graph matching"

Connected through hyperedge HE-2 (induced by source paper "multi graph matching via affinity optimization with graduated consistency regularization"):
  with members:
    - "near optimal joint object matching via convex relaxation"
    - "roml a robust feature correspondence approach for matching objects in a set of images"
    - "learning graph matching"
    - "subgraph isomorphism in planar graphs and related problems"

Connected through hyperedge HE-3 (induced by source paper "comparing graphs via persistence distortion"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "computing the gromov hausdorff distance for metric trees"
    - "measuring distance between reeb graphs"

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

Connected through hyperedge HE-5 (induced by source paper "spectral descriptors for graph matching"):
  with members:
    - "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)