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: "two are better than one an ensemble of retrieval and generation based dialog systems"
  Abstract: Open-domain human-computer conversation has attracted much attention in the field of NLP. Contrary to rule- or template-based domain-specific dialog systems, open-domain conversation usually requires data-driven approaches, which can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (called a query) in a large database, and return a reply that best matches the query. Generative approaches, typically based on recurrent neural networks (RNNs), can synthesize new replies, but they suffer from the problem of generating short, meaningless utterances. In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain. In our approach, the retrieved candidate, in addition to the original query, is fed to an RNN-based reply generator, so that the neural model is aware of more information. The generated reply is then fed back as a new candidate for post-reranking. Experimental results show that such ensemble outperforms each single part of it by a large margin.

Connected through hyperedge HE-1 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "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"

Connected through hyperedge HE-2 (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"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "say what i want towards the dark side of neural dialogue models"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "metamorphic relation based adversarial attacks on differentiable neural computer"
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "neural approaches to conversational ai"
    - "detecting egregious responses in neural sequence to sequence models"

Connected through hyperedge HE-4 (induced by source paper "extract and edit an alternative to back translation for unsupervised neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "response generation by context aware prototype editing"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "generating sentences by editing prototypes"
    - "a challenge set approach to evaluating machine translation"

Connected through hyperedge HE-5 (induced by source paper "response generation by context aware prototype editing"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "generating sentences by editing prototypes"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"
    - "opennmt open source toolkit for neural machine translation"
    - "hierarchical recurrent attention network for response generation"
    - "steering output style and topic in neural response generation"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "skeleton to response dialogue generation guided by retrieval memory"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "response generation by context aware prototype editing"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "generating sentences by editing prototypes"
    - "latent variable dialogue models and their diversity"

Connected through hyperedge HE-7 (induced by source paper "neural response ranking for social conversation a data efficient approach"):
  with members:
    - "avoiding echo responses in a retrieval based conversation system"
    - "billion scale similarity search with gpus"
    - "a practical approach to dialogue response generation in closed domains"
    - "edina building an open domain socialbot with self dialogues"
    - "rubystar a non task oriented mixture model dialog system"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "an ensemble model with ranking for social dialogue"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-8 (induced by source paper "alime assist an intelligent assistant for creating an innovative e commerce experience"):
  with members:
    - "a network based end to end trainable task oriented dialogue system"
    - "enriching word vectors with subword information"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a neural conversational model"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

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)