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: "evaluation of croatian word embeddings"
  Abstract: Croatian is poorly resourced and highly inflected language from Slavic language family. Nowadays, research is focusing mostly on English. We created a new word analogy corpus based on the original English Word2vec word analogy corpus and added some of the specific linguistic aspects from Croatian language. Next, we created Croatian WordSim353 and RG65 corpora for a basic evaluation of word similarities. We compared created corpora on two popular word representation models, based on Word2Vec tool and fastText tool. Models has been trained on 1.37B tokens training data corpus and tested on a new robust Croatian word analogy corpus. Results show that models are able to create meaningful word representation. This research has shown that free word order and the higher morphological complexity of Croatian language influences the quality of resulting word embeddings.

Connected through hyperedge HE-1 (induced by source paper "word similarity datasets for thai construction and evaluation"):
  with members:
    - "relation extraction datasets in the digital humanities domain and their evaluation with word embeddings"
    - "construction of a japanese word similarity dataset"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "the effects of data size and frequency range on distributional semantic models"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (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"
    - "enriching word vectors with subword information"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "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-3 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "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"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "issues in evaluating semantic spaces using word analogies"
    - "unsupervised dialogue act induction using gaussian mixtures"

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)