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: "the comparison of wiktionary thesauri transformed into the machine readable format"
  Abstract: Wiktionary is a unique, peculiar, valuable and original resource for natural language processing (NLP). The paper describes an open-source Wiktionary parser: its architecture and requirements followed by a description of Wiktionary features to be taken into account, some open problems of Wiktionary and the parser. The current implementation of the parser extracts the definitions, semantic relations, and translations from English and Russian Wiktionaries. The paper's goal is to interest researchers (1) in using the constructed machine-readable dictionary for different NLP tasks, (2) in extending the software to parse 170 still unused Wiktionaries. The comparison of a number and types of semantic relations, a number of definitions, and a number of translations in the English Wiktionary and the Russian Wiktionary has been carried out. It was found that the number of semantic relations in the English Wiktionary is larger by 1.57 times than in Russian (157 and 100 thousands). But the Russian Wiktionary has more "rich" entries (with a big number of semantic relations), e.g. the number of entries with three or more semantic relations is larger by 1.63 times than in the English Wiktionary. Upon comparison, it was found out the methodological shortcomings of the Wiktionary.

Connected through hyperedge HE-1 (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"
    - "transformation of wiktionary entry structure into tables and relations in a relational database schema"

Connected through hyperedge HE-2 (induced by source paper "transformation of wiktionary entry structure into tables and relations in a relational database schema"):
  with members:
    - "related terms search based on wordnet wiktionary and its application in ontology matching"
    - "index wiki database design and experiments"
    - "synonym search in wikipedia synarcher"

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)