human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "automatic identification of document translations in large multilingual document collections"):
  Abstract: Abstract Texts and their translations are a rich lin-guistic resource that can be used to train and test statistics-based Machine Translation systems and many other applications. In this paper, we present a working system that can identify translations and other very similar documents among a large number of candi-dates, by representing the document con-tents with a vector of thesaurus terms from a multilingual thesaurus, and by then measur-ing the semantic similarity between the vec-tors. Tests on different text types have shown that the system can detect transla-tions with over 96% precision in a large search space of 820 documents or more. The system was tuned to ignore language-specific similarities and to give similar documents in a second language the same similarity score as equivalent documents in the same language. The application can also be used to detect cross-lingual document plagiarism. 1 Introduction The task of mining for translational equivalences at document level presented in this paper is based on the automatic mapping of documents onto an existing multilingual knowledge structure, the

Includes members:
  Member "extending an information extraction tool set to central and eastern european languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of methods to ease the development of highly multilingual text mining applications")
  Member "cross lingual keyword assignment":
    also connected through other hyperedges:
      (none)
  Member "automatic annotation of multilingual text collections with a conceptual thesaurus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "jrc eurovoc indexer jex a freely available multi label categorisation tool")

Question: Please tell me which class the source paper 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)