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 "should all cross lingual embeddings speak english"):
  Abstract: Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we challenge these practices. First, we show that the choice of hub language can significantly impact downstream lexicon induction performance. Second, we both expand the current evaluation dictionary collection to include all language pairs using triangulation, and also create new dictionaries for under-represented languages. Evaluating established methods over all these language pairs sheds light into their suitability and presents new challenges for the field. Finally, in our analysis we identify general guidelines for strong cross-lingual embeddings baselines, based on more than just Anglocentric experiments.

Includes members:
  Member "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lost in evaluation misleading benchmarks for bilingual dictionary induction")
  Member "density matching for bilingual word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework")
  Member "bilingual lexicon induction with semi supervision in non isometric embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pmindia a collection of parallel corpora of languages of india")
  Member "lost in evaluation misleading benchmarks for bilingual dictionary induction":
    also connected through other hyperedges:
      (none)
  Member "unsupervised statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation")
  Member "unsupervised multilingual word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "learning word vectors for 157 languages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)