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: "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
  Abstract: Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is necessary to repeatedly read the arguments and dynamically exploit the efficient features useful for recognizing discourse relations. To mimic the repeated reading strategy, we propose the neural networks with multi-level attention (NNMA), combining the attention mechanism and external memories to gradually fix the attention on some specific words helpful to judging the discourse relations. Experiments on the PDTB dataset show that our proposed method achieves the state-of-art results. The visualization of the attention weights also illustrates the progress that our model observes the arguments on each level and progressively locates the important words.

Connected through hyperedge HE-1 (induced by source paper "memorizing all for implicit discourse relation recognition"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "implicit discourse relation classification via multi task neural networks"
    - "deep biaffine attention for neural dependency parsing"
    - "language modeling with gated convolutional networks"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-2 (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-3 (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"
    - "effective approaches to attention based neural machine translation"
    - "semi supervised sequence learning"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"):
  with members:
    - "spherical paragraph model"
    - "deconvolutional paragraph representation learning"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "neural network models for implicit discourse relation classification in english and chinese without surface features"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning better discourse representation for implicit discourse relation recognition via attention networks"):
  with members:
    - "variational neural discourse relation recognizer"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "battrae bidimensional attention based recursive autoencoders for learning bilingual phrase embeddings"
    - "end to end memory networks"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "ask me anything dynamic memory networks for natural language processing"
    - "recurrent models of visual attention"

Connected through hyperedge HE-6 (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"
    - "improved semantic representations from tree structured long short term memory networks"
    - "when are tree structures necessary for deep learning of representations"

Connected through hyperedge HE-7 (induced by source paper "role semantics for better models of implicit discourse relations"):
  with members:
    - "wide deep learning for recommender systems"
    - "using discourse signals for robust instructor intervention prediction"
    - "neural semantic role labeling with dependency path embeddings"
    - "linguistic input features improve neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "adversarial connective exploiting networks for implicit discourse relation classification"):
  with members:
    - "toward controlled generation of text"
    - "fast and accurate neural word segmentation for chinese"
    - "variational neural discourse relation recognizer"
    - "implicit discourse relation classification via multi task neural networks"
    - "harnessing deep neural networks with logic rules"
    - "improved techniques for training gans"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "adversarial deep averaging networks for cross lingual sentiment classification"

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)