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: "an ensemble method to produce high quality word embeddings"
  Abstract: A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space. We present an ensemble method that combines embeddings produced by GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013) with structured knowledge from the semantic networks ConceptNet (Speer and Havasi, 2012) and PPDB (Ganitkevitch et al., 2013), merging their information into a common representation with a large, multilingual vocabulary. The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations. Its score of $\rho = .596$ on an evaluation of rare words (Luong et al., 2013) is 16% higher than the previous best known system.

Connected through hyperedge HE-1 (induced by source paper "balancing the composition of word embeddings across heterogenous data sets"):
  with members:
    - "bag of tricks for efficient text classification"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-2 (induced by source paper "retro relation retrofitting for in database machine learning on textual data"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "idel in database entity linking with neural embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "sparse overcomplete word vector representations"

Connected through hyperedge HE-3 (induced by source paper "wasserstein distances for evaluating cross lingual embeddings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "a corpus for multilingual document classification in eight languages"
    - "cross lingual document retrieval using regularized wasserstein distance"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "word translation without parallel data"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-4 (induced by source paper "enhancing semantic word representations by embedding deeper word relationships"):
  with members:
    - "a comparative study of word embeddings for reading comprehension"
    - "text understanding from scratch"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "named entity recognition with bidirectional lstm cnns"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "neural machine translation by jointly learning to align and translate"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-5 (induced by source paper "into the battlefield quantifying and modeling intra community conflicts in online discussion"):
  with members:
    - "how did the discussion go discourse act classification in social media conversations"
    - "multi task learning for target dependent sentiment classification"
    - "link prediction based on graph neural networks"
    - "community interaction and conflict on the web"
    - "an army of me sockpuppets in online discussion communities"
    - "a long term analysis of polarization on twitter"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "modeling engagement dynamics of online discussions using relativistic gravitational theory"):
  with members:
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "deep reinforcement learning with a combinatorial action space for predicting and tracking popular discussion threads"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "node2vec scalable feature learning for networks"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "emotional embeddings refining word embeddings to capture emotional content of words"):
  with members:
    - "advances in pre training distributed word representations"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "counter fitting word vectors to linguistic constraints"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-8 (induced by source paper "asynchronous training of word embeddings for large text corpora"):
  with members:
    - "network efficient distributed word2vec training system for large vocabularies"
    - "efficient parallel learning of word2vec"
    - "enriching word vectors with subword information"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

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)