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: "latent variable dialogue models and their diversity"
  Abstract: We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the `boring output' issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.

Connected through hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "guiding extractive summarization with question answering rewards"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"

Connected through hyperedge HE-2 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "a study of reinforcement learning for neural machine translation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"

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"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "mixture models for diverse machine translation tricks of the trade"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "achieving human parity on automatic chinese to english news translation"
    - "scaling neural machine translation"
    - "fast decoding in sequence models using discrete latent variables"
    - "hierarchical neural story generation"
    - "sequence to sequence mixture model for diverse machine translation"
    - "understanding back translation at scale"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-5 (induced by source paper "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"):
  with members:
    - "towards universal dialogue state tracking"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "hierarchical text generation and planning for strategic dialogue"

Connected through hyperedge HE-6 (induced by source paper "adaptive parameterization for neural dialogue generation"):
  with members:
    - "breaking the activation function bottleneck through adaptive parameterization"
    - "contextual parameter generation for universal neural machine translation"
    - "attention is all you need"
    - "parlai a dialog research software platform"
    - "discovering discrete latent topics with neural variational inference"
    - "domain aware neural dialog system"
    - "a neural representation of sketch drawings"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-7 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-8 (induced by source paper "better conversations by modeling filtering and optimizing for coherence and diversity"):
  with members:
    - "improving variational encoder decoders in dialogue generation"
    - "toward controlled generation of text"
    - "a knowledge grounded neural conversation model"
    - "opennmt open source toolkit for neural machine translation"
    - "learning to decode for future success"
    - "steering output style and topic in neural response generation"
    - "adversarial learning for neural dialogue generation"
    - "why do neural dialog systems generate short and meaningless replies a comparison between dialog and translation"

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)