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 "lost in evaluation misleading benchmarks for bilingual dictionary induction"):
  Abstract: The task of bilingual dictionary induction (BDI) is commonly used for intrinsic evaluation of cross-lingual word embeddings. The largest dataset for BDI was generated automatically, so its quality is dubious. We study the composition and quality of the test sets for five diverse languages from this dataset, with concerning findings: (1) a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and (2) there are pervasive gaps in the gold-standard targets. These issues appear to affect the ranking between cross-lingual embedding systems on individual languages, and the overall degree to which the systems differ in performance. With proper nouns removed from the data, the margin between the top two systems included in the study grows from 3.4% to 17.2%. Manual verification of the predictions, on the other hand, reveals that gaps in the gold standard targets artificially inflate the margin between the two systems on English to Bulgarian BDI from 0.1% to 6.7%. We thus suggest that future research either avoids drawing conclusions from quantitative results on this BDI dataset, or accompanies such evaluation with rigorous error analysis.

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 "should all cross lingual embeddings speak english")
  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 "loss in translation learning bilingual word mapping with a retrieval criterion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective cross lingual transfer of neural machine translation models without shared vocabularies")
  Member "an iterative closest point method for unsupervised word translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zero resource cross lingual named entity recognition")
  Member "learning multilingual word embeddings in latent metric space a geometric approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "unimorph 2 0 universal morphology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linspector multilingual probing tasks for word representations")
  Member "generalizing procrustes analysis for better bilingual dictionary induction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the robustness of unsupervised and semi supervised cross lingual word embedding learning")
  Member "non adversarial unsupervised word translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)