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: "geometry of graph edit distance spaces"
  Abstract: In this paper we study the geometry of graph spaces endowed with a special class of graph edit distances. The focus is on geometrical results useful for statistical pattern recognition. The main result is the Graph Representation Theorem. It states that a graph is a point in some geometrical space, called orbit space. Orbit spaces are well investigated and easier to explore than the original graph space. We derive a number of geometrical results from the orbit space representation, translate them to the graph space, and indicate their significance and usefulness in statistical pattern recognition.

Connected through hyperedge HE-1 (induced by source paper "the mean partition theorem of consensus clustering"):
  with members:
    - "homogeneity of cluster ensembles"
    - "asymptotic behavior of mean partitions in consensus clustering"
    - "properties of the sample mean in graph spaces and the majorize minimize mean algorithm"

Connected through hyperedge HE-2 (induced by source paper "homogeneity of cluster ensembles"):
  with members:
    - "asymptotic behavior of mean partitions in consensus clustering"

Connected through hyperedge HE-3 (induced by source paper "properties of the sample mean in graph spaces and the majorize minimize mean algorithm"):
  with members:
    - "on the relation between the common labelling and the median graph"
    - "accelerating competitive learning graph quantization"
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-4 (induced by source paper "asymptotic behavior of mean partitions in consensus clustering"):
  with members:
    - "properties of the sample mean in graph spaces and the majorize minimize mean algorithm"

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)