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: "from paraphrase database to compositional paraphrase model and back"
  Abstract: The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates. However, it is still unclear how it can best be used, due to the heuristic nature of the confidences and its necessarily incomplete coverage. We propose models to leverage the phrase pairs from the PPDB to build parametric paraphrase models that score paraphrase pairs more accurately than the PPDB's internal scores while simultaneously improving its coverage. They allow for learning phrase embeddings as well as improved word embeddings. Moreover, we introduce two new, manually annotated datasets to evaluate short-phrase paraphrasing models. Using our paraphrase model trained using PPDB, we achieve state-of-the-art results on standard word and bigram similarity tasks and beat strong baselines on our new short phrase paraphrase tasks.

Connected through hyperedge HE-1 (induced by source paper "a common semantic space for monolingual and cross lingual meta embeddings"):
  with members:
    - "sequence tagging with contextual and non contextual subword representations a multilingual evaluation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "bilingual embeddings with random walks over multilingual wordnets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "think globally embed locally locally linear meta embedding of words"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "don t settle for average go for the max fuzzy sets and max pooled word vectors"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "question answering as global reasoning over semantic abstractions"):
  with members:
    - "question answering through transfer learning from large fine grained supervision data"
    - "adversarial examples for evaluating reading comprehension systems"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "semantic parsing to probabilistic programs for situated question answering"

Connected through hyperedge HE-4 (induced by source paper "parameter free sentence embedding via orthogonal basis"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-5 (induced by source paper "global locally self attentive dialogue state tracker"):
  with members:
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "one model to learn them all"
    - "a deep reinforced model for abstractive summarization"
    - "long short term memory networks for machine reading"
    - "a joint many task model growing a neural network for multiple nlp tasks"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of sentence embeddings using compositional n gram features"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "unsupervised learning of word sequence representations from scratch via convolutional tensor decomposition"
    - "enriching word vectors with subword information"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "learning distributed representations of sentences from unlabelled data"
    - "charagram embedding words and sentences via character n grams"
    - "a latent variable model approach to pmi based word embeddings"

Connected through hyperedge HE-7 (induced by source paper "aff2vec affect enriched distributional word representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "wassa 2017 shared task on emotion intensity"
    - "affect lm a neural language model for customizable affective text generation"
    - "a neural knowledge language model"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "joint word representation learning using a corpus and a semantic lexicon"

Connected through hyperedge HE-8 (induced by source paper "all but the top simple and effective postprocessing for word representations"):
  with members:
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "a latent variable model approach to pmi based word embeddings"
    - "gated feedback recurrent neural networks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)