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 word embeddings for automatic query expansion"
  Abstract: In this paper a framework for Automatic Query Expansion (AQE) is proposed using distributed neural language model word2vec. Using semantic and contextual relation in a distributed and unsupervised framework, word2vec learns a low dimensional embedding for each vocabulary entry. Using such a framework, we devise a query expansion technique, where related terms to a query are obtained by K-nearest neighbor approach. We explore the performance of the AQE methods, with and without feedback query expansion, and a variant of simple K-nearest neighbor in the proposed framework. Experiments on standard TREC ad-hoc data (Disk 4, 5 with query sets 301-450, 601-700) and web data (WT10G data with query set 451-550) shows significant improvement over standard term-overlapping based retrieval methods. However the proposed method fails to achieve comparable performance with statistical co-occurrence based feedback method such as RM3. We have also found that the word2vec based query expansion methods perform similarly with and without any feedback information.

Connected through hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "understanding the origins of bias in word embeddings"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "towards understanding linear word analogies"

Connected through hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "learning word like units from joint audio visual analysis"
    - "learning modality invariant representations for speech and images"

Connected through hyperedge HE-3 (induced by source paper "query expansion techniques for information retrieval a survey"):
  with members:
    - "just an update on pming distance for web based semantic similarity in artificial intelligence and data mining"
    - "query expansion with locally trained word embeddings"
    - "query expansion via structural motifs in wikipedia graph"
    - "query expansion based on crowd knowledge for code search"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "exploring query categorisation for query expansion a study"
    - "query expansion using term distribution and term association"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (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"
    - "representing documents and queries as sets of word embedded vectors for information retrieval"
    - "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-5 (induced by source paper "a novel model for query expansion using pseudo relevant web knowledge"):
  with members:
    - "a new approach for query expansion using wikipedia and wordnet"
    - "query expansion techniques for information retrieval a survey"

Connected through hyperedge HE-6 (induced by source paper "sequence to sequence learning for query expansion"):
  with members:
    - "ask the right questions active question reformulation with reinforcement learning"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "a large annotated corpus for learning natural language inference"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "a data driven approach to query expansion in question answering"

Connected through hyperedge HE-7 (induced by source paper "utilizing embeddings for ad hoc retrieval by document to document similarity"):
  with members:
    - "neural models for information retrieval"
    - "query expansion with locally trained word embeddings"
    - "uncertainty in neural network word embedding exploration of threshold for similarity"
    - "a dual embedding space model for document ranking"
    - "semantic regularities in document representations"
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-8 (induced by source paper "learning word relatedness over time"):
  with members:
    - "query expansion with locally trained word embeddings"
    - "history by diversity helping historians search news archives"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "distributed representations of words and phrases and their compositionality"
    - "tempeval 3 evaluating events time expressions and temporal relations"
    - "from frequency to meaning vector space models of semantics"

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)