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: "automatic annotation of multilingual text collections with a conceptual thesaurus"
  Abstract: Automatic annotation of documents with controlled vocabulary terms (descriptors) from a conceptual thesaurus is not only useful for document indexing and retrieval. The mapping of texts onto the same thesaurus furthermore allows to establish links between similar documents. This is also a substantial requirement of the Semantic Web. This paper presents an almost language-independent system that maps documents written in different languages onto the same multilingual conceptual thesaurus, EUROVOC. Conceptual thesauri differ from Natural Language Thesauri in that they consist of relatively small controlled lists of words or phrases with a rather abstract meaning. To automatically identify which thesaurus descriptors describe the contents of a document best, we developed a statistical, associative system that is trained on texts that have previously been indexed manually. In addition to describing the large number of empirically optimised parameters of the fully functional application, we present the performance of the software according to a human evaluation by professional indexers.

Connected through hyperedge HE-1 (induced by source paper "jrc eurovoc indexer jex a freely available multi label categorisation tool"):
  with members:
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"

Connected through hyperedge HE-2 (induced by source paper "an introduction to the europe media monitor family of applications"):
  with members:
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"
    - "building and displaying name relations using automatic unsupervised analysis of newspaper articles"
    - "geocoding multilingual texts recognition disambiguation and visualisation"

Connected through hyperedge HE-3 (induced by source paper "navigating multilingual news collections using automatically extracted information"):
  with members:
    - "extending an information extraction tool set to central and eastern european languages"
    - "exploiting multilingual nomenclatures and language independent text features as an interlingua for cross lingual text analysis applications"

Connected through hyperedge HE-4 (induced by source paper "automatic identification of document translations in large multilingual document collections"):
  with members:
    - "extending an information extraction tool set to central and eastern european languages"
    - "cross lingual keyword assignment"

Connected through hyperedge HE-5 (induced by source paper "exploiting multilingual nomenclatures and language independent text features as an interlingua for cross lingual text analysis applications"):
  with members:
    - "extending an information extraction tool set to central and eastern european languages"
    - "automatic identification of document translations in large multilingual document collections"

Connected through hyperedge HE-6 (induced by source paper "the jrc acquis a multilingual aligned parallel corpus with 20 languages"):
  with members:
    - "navigating multilingual news collections using automatically extracted information"
    - "exploiting multilingual nomenclatures and language independent text features as an interlingua for cross lingual text analysis applications"

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.CL(Computation and Language)