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: "adversarial advantage actor critic model for task completion dialogue policy learning"
  Abstract: This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.

Connected through hyperedge HE-1 (induced by source paper "a visual communication map for multi agent deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "knowledge grounded response generation with deep attentional latent variable model"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "understanding convolutional neural networks for text classification"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "neural approaches to conversational ai"
    - "variational memory encoder decoder"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-3 (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-4 (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"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "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-5 (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"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "discriminative deep dyna q robust planning for dialogue policy learning"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "sub domain modelling for dialogue management with hierarchical 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"
    - "towards end to end reinforcement learning of dialogue agents for information access"

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)