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: "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
  Abstract: We propose a document retrieval method for question answering that represents documents and questions as weighted centroids of word embeddings and reranks the retrieved documents with a relaxation of Word Mover's Distance. Using biomedical questions and documents from BIOASQ, we show that our method is competitive with PUBMED. With a top-k approximation, our method is fast, and easily portable to other domains and languages.

Connected through hyperedge HE-1 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-2 (induced by source paper "wasserstein fisher rao document distance"):
  with members:
    - "document similarity for texts of varying lengths via hidden topics"
    - "word mover s embedding from word2vec to document embedding"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "advances in pre training distributed word representations"
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "data driven summarization of scientific articles"):
  with members:
    - "a supervised approach to extractive summarisation of scientific papers"
    - "convolutional sequence to sequence learning"
    - "towards abstraction from extraction multiple timescale gated recurrent unit for summarization"
    - "cutting off redundant repeating generations for neural abstractive summarization"
    - "neural machine translation in linear time"
    - "fully character level neural machine translation without explicit segmentation"
    - "sequence to sequence rnns for text summarization"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "a study on passage re ranking in embedding based unsupervised semantic search"):
  with members:
    - "universal sentence encoder"
    - "bridging the gap incorporating a semantic similarity measure for effectively mapping pubmed queries to documents"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "applying deep learning to answer selection a study and an open task"
    - "skip thought vectors"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "beyond precision a study on recall of initial retrieval with neural representations"):
  with members:
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a dual embedding space model for document ranking"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "approximate nearest neighbor search on high dimensional data experiments analyses and improvement v1 0"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "query focused variable centroid vectors for passage re ranking in semantic search"):
  with members:
    - "universal sentence encoder"
    - "bridging the gap incorporating a semantic similarity measure for effectively mapping pubmed queries to documents"
    - "applying deep learning to answer selection a study and an open task"
    - "skip thought vectors"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "biomedical question answering via weighted neural network passage retrieval"):
  with members:
    - "a deep relevance matching model for ad hoc retrieval"
    - "squad 100 000 questions for machine comprehension of text"
    - "text matching as image recognition"
    - "end to end memory networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "off the beaten path let s replace term based retrieval with k nn search"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "permutation search methods are efficient yet faster search is possible"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "hashing for similarity search a survey"
    - "fast neighborhood graph search using cartesian concatenation"
    - "efficient estimation of word representations in vector space"

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)