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: "polite dialogue generation without parallel data"
  Abstract: Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, as well as paralinguistically accurate. Moreover, parallel datasets for regular-to-stylistic pairs are usually unavailable. We present three weakly-supervised models that can generate diverse polite (or rude) dialogue responses without parallel data. Our late fusion model (Fusion) merges the decoder of an encoder-attention-decoder dialogue model with a language model trained on stand-alone polite utterances. Our label-fine-tuning (LFT) model prepends to each source sequence a politeness-score scaled label (predicted by our state-of-the-art politeness classifier) during training, and at test time is able to generate polite, neutral, and rude responses by simply scaling the label embedding by the corresponding score. Our reinforcement learning model (Polite-RL) encourages politeness generation by assigning rewards proportional to the politeness classifier score of the sampled response. We also present two retrieval-based polite dialogue model baselines. Human evaluation validates that while the Fusion and the retrieval-based models achieve politeness with poorer context-relevance, the LFT and Polite-RL models can produce significantly more polite responses without sacrificing dialogue quality.

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 "avgout a simple output probability measure to eliminate dull responses"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "improving abstraction in text summarization"
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "learning to decode for future success"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"

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"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-4 (induced by source paper "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "text generation with exemplar based adaptive decoding"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "towards generating long and coherent text with multi level latent variable models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"

Connected through hyperedge HE-5 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-6 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning based text style transfer without parallel training corpus"):
  with members:
    - "improving reinforcement learning based image captioning with natural language prior"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "a study of reinforcement learning for neural machine translation"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "word mover s embedding from word2vec to document embedding"

Connected through hyperedge HE-8 (induced by source paper "adapting language models for non parallel author stylized rewriting"):
  with members:
    - "a lexical syntactic and semantic perspective for understanding style in text"
    - "mass masked sequence to sequence pre training for language generation"
    - "evaluating style transfer for text"
    - "cross lingual language model pretraining"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)