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: "adversarial connective exploiting networks for implicit discourse relation classification"
  Abstract: Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.

Connected through hyperedge HE-1 (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"
    - "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"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "when are tree structures necessary for deep learning of representations"

Connected through hyperedge HE-3 (induced by source paper "on unifying deep generative models"):
  with members:
    - "toward controlled generation of text"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "harnessing deep neural networks with logic rules"
    - "conditional image generation with pixelcnn decoders"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (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"

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)