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: "topic2vec learning distributed representations of topics"
  Abstract: Latent Dirichlet Allocation (LDA) mining thematic structure of documents plays an important role in nature language processing and machine learning areas. However, the probability distribution from LDA only describes the statistical relationship of occurrences in the corpus and usually in practice, probability is not the best choice for feature representations. Recently, embedding methods have been proposed to represent words and documents by learning essential concepts and representations, such as Word2Vec and Doc2Vec. The embedded representations have shown more effectiveness than LDA-style representations in many tasks. In this paper, we propose the Topic2Vec approach which can learn topic representations in the same semantic vector space with words, as an alternative to probability. The experimental results show that Topic2Vec achieves interesting and meaningful results.

Connected through hyperedge HE-1 (induced by source paper "improving document classification with multi sense embeddings"):
  with members:
    - "topic modeling in embedding spaces"
    - "the dynamic embedded topic model"
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "probabilistic fasttext for multi sense word embeddings"
    - "efficient vector representation for documents through corruption"

Connected through hyperedge HE-2 (induced by source paper "sequence to sequence networks for roman urdu to urdu transliteration"):
  with members:
    - "machine translation approaches and survey for indian languages"
    - "recent trends in deep learning based natural language processing"
    - "translation of patent sentences with a large vocabulary of technical terms using neural machine translation"
    - "sentiment analysis of citations using word2vec"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "character based neural machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "eve explainable vector based embedding technique using wikipedia"):
  with members:
    - "vector embedding of wikipedia concepts and entities"
    - "query expansion with locally trained word embeddings"
    - "why should i trust you explaining the predictions of any classifier"
    - "a latent variable model approach to pmi based word embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "roget s thesaurus as a lexical resource for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "tweet2vec character based distributed representations for social media"):
  with members:
    - "finding function in form compositional character models for open vocabulary word representation"
    - "skip thought vectors"
    - "character aware neural language models"
    - "visualizing and understanding recurrent networks"
    - "boosting named entity recognition with neural character embeddings"
    - "distributed representations of sentences and documents"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "scdv sparse composite document vectors using soft clustering over distributional representations"):
  with members:
    - "ltsg latent topical skip gram for mutually learning topic model and vector representations"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "representing documents and queries as sets of word embedded vectors for information retrieval"
    - "topic modeling using distributed word embeddings"
    - "words are not equal graded weighting model for building composite document vectors"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "a practical algorithm for topic modeling with provable guarantees"

Connected through hyperedge HE-6 (induced by source paper "learning to retrieve out of vocabulary words in speech recognition"):
  with members:
    - "distributed representations of sentences and documents"
    - "word2vec parameter learning explained"
    - "neural machine translation by jointly learning to align and translate"
    - "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)