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: "deep reinforcement learning for multi domain dialogue systems"
  Abstract: Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems.

Connected through hyperedge HE-1 (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"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "integrating planning for task completion dialogue policy learning"):
  with members:
    - "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"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "the predictron end to end learning and planning"

Connected through hyperedge HE-3 (induced by source paper "subgoal discovery for hierarchical dialogue policy learning"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "feudal networks for hierarchical reinforcement learning"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "feudal reinforcement learning for dialogue management in large domains"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "continuously learning neural dialogue management"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-6 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "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 "sub domain modelling for dialogue management with hierarchical reinforcement learning"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "continuously learning neural dialogue management"
    - "policy networks with two stage training for dialogue systems"
    - "benchmarking deep reinforcement learning for continuous control"
    - "playing atari with deep reinforcement learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-8 (induced by source paper "online sequence to sequence active learning for open domain dialogue generation"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "agent agnostic human in the loop reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a conditional variational framework for dialog generation"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "investigation of language understanding impact for reinforcement learning based 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.AI(Artificial Intelligence)