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 "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"):
  Abstract: Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. Recent developments which construct these embeddings by aligning monolingual spaces have shown that accurate alignments can be obtained with little or no supervision. However, the focus has been on a particular controlled scenario for evaluation, and there is no strong evidence on how current state-of-the-art systems would fare with noisy text or for language pairs with major linguistic differences. In this paper we present an extensive evaluation over multiple cross-lingual embedding models, analyzing their strengths and limitations with respect to different variables such as target language, training corpora and amount of supervision. Our conclusions put in doubt the view that high-quality cross-lingual embeddings can always be learned without much supervision.

Includes members:
  Member "a survey of cross lingual word embedding models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual alignment of contextual word representations")
  Member "learning cross lingual word embeddings from twitter via distant supervision":
    also connected through other hyperedges:
      (none)
  Member "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "why is unsupervised alignment of english embeddings from different algorithms so hard":
    also connected through other hyperedges:
      (none)
  Member "the limitations of cross language word embeddings evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meemi a simple method for post processing cross lingual word embeddings")
  Member "xnli evaluating cross lingual sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "unsupervised multilingual word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "on difficulties of cross lingual transfer with order differences a case study on dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "xpersona evaluating multilingual personalized chatbot")

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)