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: "on the relation between the common labelling and the median graph"
  Abstract: In structural pattern recognition, given a set of graphs, the computation of a Generalized Median Graph is a well known problem. Some methods approach the problem by assuming a relation between the Generalized Median Graph and the Common Labelling problem. However, this relation has still not been formally proved. In this paper, we analyse such relation between both problems. The main result proves that the cost of the common labelling upper-bounds the cost of the median with respect to the given set. In addition, we show that the two problems are equivalent in some cases.

Connected through hyperedge HE-1 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping"):
  with members:
    - "hardness of consensus problems for circular strings and time series averaging"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "properties of the sample mean in graph spaces and the majorize minimize mean algorithm"):
  with members:
    - "geometry of graph edit distance spaces"
    - "accelerating competitive learning graph quantization"
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"
    - "benchmarking optimization software with performance profiles"

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)