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: "transformation of wiktionary entry structure into tables and relations in a relational database schema"
  Abstract: This paper addresses the question of automatic data extraction from the Wiktionary, which is a multilingual and multifunctional dictionary. Wiktionary is a collaborative project working on the same principles as the Wikipedia. The Wiktionary entry is a plain text from the text processing point of view. Wiktionary guidelines prescribe the entry layout and rules, which should be followed by editors of the dictionary. The presence of the structure of a Wiktionary article and formatting rules allows transforming the Wiktionary entry structure into tables and relations in a relational database schema, which is a part of a machine-readable dictionary (MRD). The paper describes how the flat text of the Wiktionary entry was extracted, converted, and stored in the specially designed relational database. The MRD contains the definitions, semantic relations, and translations extracted from the English and Russian Wiktionaries. The parser software is released under the open source license agreement (GPL), to facilitate its dissemination, modification and upgrades, to draw researchers and programmers into parsing other Wiktionaries, not only Russian and English.

Connected through hyperedge HE-1 (induced by source paper "analysis of the quotation corpus of the russian wiktionary"):
  with members:
    - "multilingual ontology matching based on wiktionary data accessible via sparql endpoint"

Connected through hyperedge HE-2 (induced by source paper "multilingual ontology matching based on wiktionary data accessible via sparql endpoint"):
  with members:
    - "an empirical study of real world sparql queries"
    - "the comparison of wiktionary thesauri transformed into the machine readable format"

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.IR(Information Retrieval)