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: "a latent variable recurrent neural network for discourse relation language models"
  Abstract: This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-of-the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.

Connected through hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "guiding extractive summarization with question answering rewards"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"

Connected through hyperedge HE-2 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process"):
  with members:
    - "unsupervised dialog structure learning"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "what gets echoed understanding the pointers in explanations of persuasive arguments"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "attentive interaction model modeling changes in view in argumentation"
    - "joint modeling of content and discourse relations in dialogues"
    - "winning on the merits the joint effects of content and style on debate outcomes"

Connected through hyperedge HE-3 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "self attention with relative position representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "a deep sequential model for discourse parsing on multi party dialogues"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "content word based sentence decoding and evaluating for open domain neural response generation"):
  with members:
    - "universal dependency parsing from scratch"
    - "coarse to fine decoding for neural semantic parsing"
    - "toward controlled generation of text"
    - "latent intention dialogue models"
    - "event representations for automated story generation with deep neural nets"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"

Connected through hyperedge HE-5 (induced by source paper "what you say and how you say it joint modeling of topics and discourse in microblog conversations"):
  with members:
    - "improving topic models with latent feature word representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "joint modeling of content and discourse relations in dialogues"
    - "find the conversation killers a predictive study of thread ending posts"
    - "discovering discrete latent topics with neural variational inference"
    - "jointly learning word embeddings and latent topics"

Connected through hyperedge HE-6 (induced by source paper "neural generative rhetorical structure parsing"):
  with members:
    - "a unified linear time framework for sentence level discourse parsing"
    - "variational smoothing in recurrent neural network language models"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "neural discourse structure for text categorization"
    - "cross lingual rst discourse parsing"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-7 (induced by source paper "reconhecimento de actos de dialogo hierarquicos e multi etiqueta em dados em espanhol"):
  with members:
    - "a study on dialog act recognition using character level tokenization"
    - "very deep convolutional networks for text classification"
    - "mapping the dialog act annotations of the lego corpus into the communicative functions of iso 24617 2"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "the influence of context on dialogue act recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "deep dialog act recognition using multiple token segment and context information representations"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "a study on dialog act recognition using character level tokenization"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "very deep convolutional networks for text classification"
    - "enriching word vectors with subword information"

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)