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: "conditional generation and snapshot learning in neural dialogue systems"
  Abstract: Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.

Connected through hyperedge HE-1 (induced by source paper "moss end to end dialog system framework with modular supervision"):
  with members:
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "decoupling strategy and generation in negotiation dialogues"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "end to end task completion neural dialogue systems"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-2 (induced by source paper "effective data augmentation approaches to end to end task oriented dialogue"):
  with members:
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "talking to myself self dialogues as data for conversational agents"
    - "automatic data expansion for customer care spoken language understanding"
    - "sequence to sequence data augmentation for dialogue language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"

Connected through hyperedge HE-3 (induced by source paper "semantically conditioned dialog response generation via hierarchical disentangled self attention"):
  with members:
    - "xl nbt a cross lingual neural belief tracking framework"
    - "global locally self attentive dialogue state tracker"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"
    - "latent intention dialogue models"
    - "attention is all you need"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

Connected through hyperedge HE-4 (induced by source paper "gecor an end to end generative ellipsis and co reference resolution model for task oriented dialogue"):
  with members:
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "get to the point summarization with pointer generator networks"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "deep reinforcement learning for dialogue generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "deep reinforcement learning for mention ranking coreference models"
    - "a neural conversational model"

Connected through hyperedge HE-5 (induced by source paper "disentangling language and knowledge in task oriented dialogs"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "get to the point summarization with pointer generator networks"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "pointing the unknown words"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of interpretable dialog models"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "neural discrete representation learning"
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-7 (induced by source paper "latent intention dialogue models"):
  with members:
    - "latent variable dialogue models and their diversity"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "on line active reward learning for policy optimisation in spoken dialogue systems"
    - "dialog based language learning"
    - "language as a latent variable discrete generative models for sentence compression"
    - "neural belief tracker data driven dialogue state tracking"

Connected through hyperedge HE-8 (induced by source paper "iterative policy learning in end to end trainable task oriented neural dialog models"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "on line active reward learning for policy optimisation in spoken dialogue systems"

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)