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: "retrieve and refine improved sequence generation models for dialogue"
  Abstract: Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it -- the final sequence generator treating the retrieval as additional context. We show on the recent CONVAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations.

Connected through hyperedge HE-1 (induced by source paper "the second conversational intelligence challenge convai2"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "data augmentation for neural online chat response selection"
    - "dialogue natural language inference"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

Connected through hyperedge HE-3 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"

Connected through hyperedge HE-4 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"
    - "contextual explanation networks"

Connected through hyperedge HE-5 (induced by source paper "moel mixture of empathetic listeners"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "skeleton to response dialogue generation guided by retrieval memory"

Connected through hyperedge HE-6 (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"
    - "disentangled representation learning for non parallel text style transfer"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-7 (induced by source paper "retrieval based goal oriented dialogue generation"):
  with members:
    - "scaling multi domain dialogue state tracking via query reformulation"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "on extractive and abstractive neural document summarization with transformer language models"
    - "towards universal dialogue state tracking"
    - "a strong baseline for question relevancy ranking"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"
    - "scalable multi domain dialogue state tracking"

Connected through hyperedge HE-8 (induced by source paper "non parametric adaptation for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "improving the transformer translation model with document level context"
    - "guiding neural machine translation with retrieved translation pieces"
    - "retrieval based neural code generation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "generating sentences by editing prototypes"

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)