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: "information distance in multiples"
  Abstract: Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering, and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minimal overlap, additivity, and normalized information distance in multiples. We use the theoretical notion of Kolmogorov complexity which for practical purposes is approximated by the length of the compressed version of the file involved, using a real-world compression program. #R##N#{\em Index Terms}-- Information distance, multiples, pattern recognition, data mining, similarity, Kolmogorov complexity

Connected through hyperedge HE-1 (induced by source paper "using data compressors for statistical analysis of problems on homogeneity testing and classification"):
  with members:
    - "the similarity metric"

Connected through hyperedge HE-2 (induced by source paper "normalized compression distance of multisets with applications"):
  with members:
    - "multi column deep neural networks for image classification"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-3 (induced by source paper "exact expression for information distance"):
  with members:
    - "variations on muchnik s conditional complexity theorem"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-4 (induced by source paper "exploring programmable self assembly in non dna based molecular computing"):
  with members:
    - "information distance new developments"
    - "on the dynamic qualitative behaviour of universal computation"
    - "compression based investigation of the dynamical properties of cellular automata and other systems"
    - "the computational power of benenson automata"
    - "the similarity metric"

Connected through hyperedge HE-5 (induced by source paper "normalized google distance of multisets with applications"):
  with members:
    - "normalized compression distance of multisets with applications"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-6 (induced by source paper "information distance new developments"):
  with members:
    - "a fast quartet tree heuristic for hierarchical clustering"
    - "the similarity metric"

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)