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: "learning from real users rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems"
  Abstract: To train a statistical spoken dialogue system (SDS) it is essential that an accurate method for measuring task success is available. To date training has relied on presenting a task to either simulated or paid users and inferring the dialogue's success by observing whether this presented task was achieved or not. Our aim however is to be able to learn from real users acting under their own volition, in which case it is non-trivial to rate the success as any prior knowledge of the task is simply unavailable. User feedback may be utilised but has been found to be inconsistent. Hence, here we present two neural network models that evaluate a sequence of turn-level features to rate the success of a dialogue. Importantly these models make no use of any prior knowledge of the user's task. The models are trained on dialogues generated by a simulated user and the best model is then used to train a policy on-line which is shown to perform at least as well as a baseline system using prior knowledge of the user's task. We note that the models should also be of interest for evaluating SDS and for monitoring a dialogue in rule-based SDS.

Connected through hyperedge HE-1 (induced by source paper "improving interaction quality estimation with bilstms and the impact on dialogue policy learning"):
  with members:
    - "attention is all you need"
    - "on line active reward learning for policy optimisation in spoken dialogue systems"
    - "an application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email"

Connected through hyperedge HE-2 (induced by source paper "adversarial learning of task oriented neural dialog models"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "sentiment adaptive end to end dialog systems"):
  with members:
    - "learning conversational systems that interleave task and non task content"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "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"
    - "query regression networks for machine comprehension"

Connected through hyperedge HE-4 (induced by source paper "learning end to end goal oriented dialog with multiple answers"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "question dependent recurrent entity network for question answering"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "a network based end to end trainable task oriented dialogue system"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "end to end memory networks"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-5 (induced by source paper "a deep reinforcement learning chatbot"):
  with members:
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "parlai a dialog research software platform"
    - "emergence of grounded compositional language in multi agent populations"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "maca a modular architecture for conversational agents"
    - "dialport connecting the spoken dialog research community to real user data"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-6 (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"
    - "conditional generation and snapshot learning in neural dialogue systems"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning of speech recognition system based on policy gradient and hypothesis selection"):
  with members:
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "generative deep neural networks for dialogue a short review"):
  with members:
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "smart reply automated response suggestion for email"
    - "dialog based language learning"
    - "learning end to end goal oriented dialog"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "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.LG(Machine Learning)