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: "bridging the gap a semantic similarity measure between queries and documents"
  Abstract: The main approach of traditional information retrieval (IR) is to examine how many words from a query appear in a document. A drawback of this approach, however, is that it may fail to detect relevant documents where no or only few words from a query are found. The semantic analysis methods such as LSA (latent semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to address the issue, but their performance is not superior compared to common IR approaches. Here we present a query-document similarity measure motivated by the Word Mover's Distance. Unlike other similarity measures, the proposed method relies on neural word embeddings to calculate the distance between words. Our method is efficient and straightforward to implement. The experimental results on TREC and PubMed show that our approach provides significantly better performance than BM25. We also discuss the pros and cons of our approach and show that there is a synergy effect when the word embedding measure is combined with the BM25 function.

Connected through hyperedge HE-1 (induced by source paper "semantically informed distance and similarity measures for paraphrase plagiarism identification"):
  with members:
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "edit distance for pushdown automata"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "neural information retrieval a literature review"):
  with members:
    - "lstm a search space odyssey"
    - "tweet2vec character based distributed representations for social media"
    - "using word embeddings for automatic query expansion"
    - "representing documents and queries as sets of word embedded vectors for information retrieval"
    - "using word embeddings in twitter election classification"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "toward a deep neural approach for knowledge based ir"

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)