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: "a new similarity measure for taxonomy based on edge counting"
  Abstract: This paper introduces a new similarity measure based on edge counting in a taxonomy like WorldNet or Ontology. Measurement of similarity between text segments or concepts is very useful for many applications like information retrieval, ontology matching, text mining, and question answering and so on. Several measures have been developed for measuring similarity between two concepts: out of these we see that the measure given by Wu and Palmer [1] is simple, and gives good performance. Our measure is based on their measure but strengthens it. Wu and Palmer [1] measure has a disadvantage that it does not consider how far the concepts are semantically. In our measure we include the shortest path between the concepts and the depth of whole taxonomy together with the distances used in Wu and Palmer [1]. Also the measure has following disadvantage i.e. in some situations, the similarity of two elements of an IS-A ontology contained in the neighborhood exceeds the similarity value of two elements contained in the same hierarchy. Our measure introduces a penalization factor for this case based upon shortest length between the concepts and depth of whole taxonomy.

Connected through hyperedge HE-1 (induced by source paper "memetic eda based approaches to comprehensive quality aware automated semantic web service composition"):
  with members:
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-2 (induced by source paper "semantic measures for the comparison of units of language concepts or entities from text and knowledge base analysis"):
  with members:
    - "description and evaluation of semantic similarity measures approaches"
    - "distributional measures as proxies for semantic relatedness"
    - "roget s thesaurus and semantic similarity"
    - "distributional measures of semantic distance a survey"
    - "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge"
    - "serimi resource description similarity rdf instance matching and interlinking"
    - "hitting and commute times in large graphs are often misleading"
    - "effectively integrating information content and structural relationship to improve the go based similarity measure between proteins"

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.AI(Artificial Intelligence)