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: "response generation by context aware prototype editing"
  Abstract: Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response generation, that is response generation by editing, which significantly increases the diversity and informativeness of the generation results. Our assumption is that a plausible response can be generated by slightly revising an existing response prototype. The prototype is retrieved from a pre-defined index and provides a good start-point for generation because it is grammatical and informative. We design a response editing model, where an edit vector is formed by considering differences between a prototype context and a current context, and then the edit vector is fed to a decoder to revise the prototype response for the current context. Experiment results on a large scale dataset demonstrate that the response editing model outperforms generative and retrieval-based models on various aspects.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "a hybrid retrieval generation neural conversation model"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"

Connected through hyperedge HE-4 (induced by source paper "coupling retrieval and meta learning for context dependent semantic parsing"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "dictionary guided editing networks for paraphrase generation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "mapping language to code in programmatic context"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "spherical latent spaces for stable variational autoencoders"
    - "sequence to action end to end semantic graph generation for semantic parsing"

Connected through hyperedge HE-5 (induced by source paper "extract and edit an alternative to back translation for unsupervised neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "how to write summaries with patterns learning towards abstractive summarization through prototype editing"):
  with members:
    - "gsn a graph structured network for multi party dialogues"
    - "abstractive text summarization by incorporating reader comments"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "learning to write with cooperative discriminators"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "iterative document representation learning towards summarization with polishing"

Connected through hyperedge HE-7 (induced by source paper "unsupervised context rewriting for open domain conversation"):
  with members:
    - "a study of reinforcement learning for neural machine translation"
    - "hierarchical recurrent attention network for response generation"
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "topic aware neural response generation"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

Connected through hyperedge HE-8 (induced by source paper "improving knowledge aware dialogue generation via knowledge base question answering"):
  with members:
    - "learning to select knowledge for response generation in dialog systems"
    - "a knowledge grounded neural conversation model"
    - "improved neural relation detection for knowledge base question answering"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "deep reinforcement learning for dialogue generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "question answering on freebase via relation extraction and textual evidence"
    - "building end to end dialogue systems using generative hierarchical neural network models"

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)