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: "representing documents and queries as sets of word embedded vectors for information retrieval"
  Abstract: A major difficulty in applying word vector embeddings in IR is in devising an effective and efficient strategy for obtaining representations of compound units of text, such as whole documents, (in comparison to the atomic words), for the purpose of indexing and scoring documents. Instead of striving for a suitable method for obtaining a single vector representation of a large document of text, we rather aim for developing a similarity metric that makes use of the similarities between the individual embedded word vectors in a document and a query. More specifically, we represent a document and a query as sets of word vectors, and use a standard notion of similarity measure between these sets, computed as a function of the similarities between each constituent word pair from these sets. We then make use of this similarity measure in combination with standard IR based similarities for document ranking. The results of our initial experimental investigations shows that our proposed method improves MAP by up to $5.77\%$, in comparison to standard text-based language model similarity, on the TREC ad-hoc dataset.

Connected through hyperedge HE-1 (induced by source paper "hierarchical document encoder for parallel corpus mining"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "word mover s embedding from word2vec to document embedding"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning cross lingual sentence representations via a multi task dual encoder model"
    - "efficient vector representation for documents through corruption"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "affect enriched word embeddings for news information retrieval"):
  with members:
    - "toward incorporation of relevant documents in word2vec"
    - "relevance based word embedding"
    - "using word embeddings for automatic query expansion"
    - "a dual embedding space model for document ranking"
    - "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "extracting parallel paragraphs from common crawl"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (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"
    - "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"
    - "bridging the gap a semantic similarity measure between queries and documents"

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"
    - "topic modeling using distributed word embeddings"
    - "topic2vec learning distributed representations of topics"
    - "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"

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.IR(Information Retrieval)