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: "one vector is not enough entity augmented distributional semantics for discourse relations"
  Abstract: Discourse relations bind smaller linguistic units into coherent texts. However, automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lower-level components, such as entity mentions. Our solution computes distributional meaning representations by composition up the syntactic parse tree. A key difference from previous work on compositional distributional semantics is that we also compute representations for entity mentions, using a novel downward compositional pass. Discourse relations are predicted from the distributional representations of the arguments, and also of their coreferent entity mentions. The resulting system obtains substantial improvements over the previous state-of-the-art in predicting implicit discourse relations in the Penn Discourse Treebank.

Connected through hyperedge HE-1 (induced by source paper "deep enhanced representation for implicit discourse relation recognition"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a structured self attentive sentence embedding"
    - "language modeling with gated convolutional networks"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-2 (induced by source paper "learning to explicitate connectives with seq2seq network for implicit discourse relation classification"):
  with members:
    - "acquiring annotated data with cross lingual explicitation for implicit discourse relation classification"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "adversarial connective exploiting networks for implicit discourse relation classification"
    - "implicit discourse relation classification via multi task neural networks"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "effective approaches to attention based neural machine translation"
    - "semi supervised sequence learning"

Connected through hyperedge HE-3 (induced by source paper "tag enhanced tree structured neural networks for implicit discourse relation classification"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "textual entailment with structured attentions and composition"
    - "adversarial connective exploiting networks for implicit discourse relation classification"
    - "tree to sequence attentional neural machine translation"
    - "enhanced lstm for natural language inference"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-4 (induced by source paper "acquiring annotated data with cross lingual explicitation for implicit discourse relation classification"):
  with members:
    - "learning to explicitate connectives with seq2seq network for implicit discourse relation classification"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "a pdtb styled end to end discourse parser"

Connected through hyperedge HE-5 (induced by source paper "dissent sentence representation learning from explicit discourse relations"):
  with members:
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "automatic prediction of discourse connectives"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-6 (induced by source paper "a latent variable recurrent neural network for discourse relation language models"):
  with members:
    - "importance weighted autoencoders"
    - "document context language models"
    - "a recurrent latent variable model for sequential data"
    - "transition based dependency parsing with stack long short term memory"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural adaptive sequential monte carlo"
    - "larger context language modelling"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "using sentence level lstm language models for script inference"):
  with members:
    - "learning scripts as hidden markov models"
    - "exploring the limits of language modeling"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "skip thought vectors"
    - "teaching machines to read and comprehend"
    - "multi task sequence to sequence learning"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "shallow discourse parsing using distributed argument representations and bayesian optimization"):
  with members:
    - "when are tree structures necessary for deep learning of representations"

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)