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: "new word analogy corpus for exploring embeddings of czech words"
  Abstract: The word embedding methods have been proven to be very useful in many tasks of NLP (Natural Language Processing). Much has been investigated about word embeddings of English words and phrases, but only little attention has been dedicated to other languages. #R##N#Our goal in this paper is to explore the behavior of state-of-the-art word embedding methods on Czech, the language that is characterized by very rich morphology. We introduce new corpus for word analogy task that inspects syntactic, morphosyntactic and semantic properties of Czech words and phrases. We experiment with Word2Vec and GloVe algorithms and discuss the results on this corpus. The corpus is available for the research community.

Connected through hyperedge HE-1 (induced by source paper "evaluation of greek word embeddings"):
  with members:
    - "word embeddings from large scale greek web content"
    - "learning word vectors for 157 languages"
    - "evaluation of croatian word embeddings"
    - "enriching word vectors with subword information"
    - "wordrep a benchmark for research on learning word representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "word embeddings for the armenian language intrinsic and extrinsic evaluation"):
  with members:
    - "pioner datasets and baselines for armenian named entity recognition"
    - "learning word vectors for 157 languages"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "sart similarity analogies and relatedness for tatar language new benchmark datasets for word embeddings evaluation"):
  with members:
    - "enriching word vectors with subword information"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "evaluation of croatian word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "issues in evaluating semantic spaces using word analogies"
    - "unsupervised dialogue act induction using gaussian mixtures"

Connected through hyperedge HE-5 (induced by source paper "learning word vectors for 157 languages"):
  with members:
    - "portuguese word embeddings evaluating on word analogies and natural language tasks"
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "enriching word vectors with subword information"):
  with members:
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"
    - "alternative structures for character level rnns"
    - "compositional morphology for word representations and language modelling"
    - "efficient estimation of word representations in vector space"

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)