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: "neural response generation with dynamic vocabularies"
  Abstract: We study response generation for open domain conversation in chatbots. Existing methods assume that words in responses are generated from an identical vocabulary regardless of their inputs, which not only makes them vulnerable to generic patterns and irrelevant noise, but also causes a high cost in decoding. We propose a dynamic vocabulary sequence-to-sequence (DVS2S) model which allows each input to possess their own vocabulary in decoding. In training, vocabulary construction and response generation are jointly learned by maximizing a lower bound of the true objective with a Monte Carlo sampling method. In inference, the model dynamically allocates a small vocabulary for an input with the word prediction model, and conducts decoding only with the small vocabulary. Because of the dynamic vocabulary mechanism, DVS2S eludes many generic patterns and irrelevant words in generation, and enjoys efficient decoding at the same time. Experimental results on both automatic metrics and human annotations show that DVS2S can significantly outperform state-of-the-art methods in terms of response quality, but only requires 60% decoding time compared to the most efficient baseline.

Connected through hyperedge HE-1 (induced by source paper "condition transforming variational autoencoder for conversation response generation"):
  with members:
    - "a knowledge grounded neural conversation model"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "enhanced lstm for natural language inference"
    - "topic aware neural response generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-2 (induced by source paper "improving matching models with hierarchical contextualized representations for multi turn response selection"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "hierarchical recurrent attention network for response generation"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-3 (induced by source paper "incorporating relevant knowledge in context modeling and response generation"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "a knowledge grounded neural conversation model"
    - "latent intention dialogue models"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a conditional variational framework for dialog generation"

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)