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 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

Connected through hyperedge HE-1 (induced by source paper "gebiotoolkit automatic extraction of gender balanced multilingual corpus of wikipedia biographies"):
  with members:
    - "evaluating gender bias in machine translation"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "gender bias in coreference resolution"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "reducing gender bias in abusive language detection"

Connected through hyperedge HE-2 (induced by source paper "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"):
  with members:
    - "learning joint multilingual sentence representations with neural machine translation"
    - "learned in translation contextualized word vectors"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "fully character level neural machine translation without explicit segmentation"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-3 (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 annotation of multilingual text collections with a conceptual thesaurus"

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)