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: "coherent dialogue with attention based language models"
  Abstract: We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism. Our attention-RNN language model dynamically increases the scope of attention on the history as the conversation continues, as opposed to standard attention (or alignment) models with a fixed input scope in a sequence-to-sequence model. This allows each generated word to be associated with the most relevant words in its corresponding conversation history. We evaluate the model on two popular dialogue datasets, the open-domain MovieTriples dataset and the closed-domain Ubuntu Troubleshoot dataset, and achieve significant improvements over the state-of-the-art and baselines on several metrics, including complementary diversity-based metrics, human evaluation, and qualitative visualizations. We also show that a vanilla RNN with dynamic attention outperforms more complex memory models (e.g., LSTM and GRU) by allowing for flexible, long-distance memory. We promote further coherence via topic modeling-based reranking.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-2 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents"):
  with members:
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "relevance promoting language model for short text conversation"):
  with members:
    - "generating multiple diverse responses with multi mapping and posterior mapping selection"
    - "dlgnet a transformer based model for dialogue response generation"
    - "the curious case of neural text degeneration"
    - "comparison of diverse decoding methods from conditional language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "a discrete cvae for response generation on short text conversation"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-5 (induced by source paper "reflective decoding network for image captioning"):
  with members:
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "mattnet modular attention network for referring expression comprehension"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "self attentive residual decoder for neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "knowledge grounded response generation with deep attentional latent variable model"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "understanding convolutional neural networks for text classification"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "neural approaches to conversational ai"
    - "variational memory encoder decoder"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-7 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"

Connected through hyperedge HE-8 (induced by source paper "tigs an inference algorithm for text infilling with gradient search"):
  with members:
    - "text infilling"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "detecting egregious responses in neural sequence to sequence models"
    - "a multi modal chinese poetry generation model"
    - "bidirectional beam search forward backward inference in neural sequence models for fill in the blank image captioning"
    - "attention is all you need"

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)