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 embedding space explaining cross lingual task performance with eigenvalue divergence"):
  Abstract: Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work, we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging. We propose a novel language distance measure, Eigenvalue Divergence (EVD), which quantifies the degree of isomorphism between two monolingual spaces. We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks, 2) EVD outperforms other standard embedding-based language distance measures across the board, at the same time being computationally more tractable and easier to interpret. Finally, we demonstrate that EVD captures information which is complementary to typologically driven language distance measures. We report that their combination yields even higher correlations with performance levels in all cross-lingual tasks.

Includes members:
  Member "on the robustness of unsupervised and semi supervised cross lingual word embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "a survey of cross lingual word embedding models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual alignment of contextual word representations")
  Member "what do language representations really represent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "analyzing the limitations of cross lingual word embedding mappings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "bilingual lexicon induction with semi supervision in non isometric embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework")
  Member "investigating multilingual nmt representations at scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "contextual lensing of universal sentence representations")
  Member "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refinement of unsupervised cross lingual word embeddings")
  Member "do we really need fully unsupervised cross lingual embeddings":
    also connected through other hyperedges:
      (none)

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)