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: "discriminative deep dyna q robust planning for dialogue policy learning"
  Abstract: This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ's high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent's capability of adapting to a changing environment is tested.

Connected through hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "domain adaptive dialog generation via meta learning"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "unsupervised dialog structure learning"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"

Connected through hyperedge HE-2 (induced by source paper "self supervised dialogue learning"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "self supervised learning for contextualized extractive summarization"
    - "cross lingual language model pretraining"
    - "adversarial learning of task oriented neural dialog models"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "budgeted policy learning for task oriented dialogue systems"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a tutorial on thompson sampling"
    - "end to end task completion neural dialogue systems"

Connected through hyperedge HE-4 (induced by source paper "bridging dialogue generation and facial expression synthesis"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "integrating planning for task completion dialogue policy learning"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (induced by source paper "guided dialog policy learning reward estimation for multi domain task oriented dialog"):
  with members:
    - "convlab multi domain end to end dialog system platform"
    - "adversarial learning of task oriented neural dialog models"
    - "user modeling for task oriented dialogues"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sentiment adaptive end to end dialog systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "dual supervised learning for natural language understanding and generation"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "integrating planning for task completion dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "dual supervised learning"
    - "listening while speaking speech chain by deep learning"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"

Connected through hyperedge HE-7 (induced by source paper "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"):
  with members:
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "a tutorial on thompson sampling"
    - "end to end task completion neural dialogue systems"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "a user simulator for task completion dialogues"
    - "continuously learning neural dialogue management"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"

Connected through hyperedge HE-8 (induced by source paper "dynamically context sensitive time decay attention for dialogue modeling"):
  with members:
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "end to end task completion neural dialogue systems"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "knowledge as a teacher knowledge guided structural attention networks"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"

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)