human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "guided dialog policy learning reward estimation for multi domain task oriented dialog"):
  Abstract: Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals. With the growing needs to handle complex goals across multiple domains, such manually designed reward functions are not affordable to deal with the complexity of real-world tasks. To this end, we propose Guided Dialog Policy Learning, a novel algorithm based on Adversarial Inverse Reinforcement Learning for joint reward estimation and policy optimization in multi-domain task-oriented dialog. The proposed approach estimates the reward signal and infers the user goal in the dialog sessions. The reward estimator evaluates the state-action pairs so that it can guide the dialog policy at each dialog turn. Extensive experiments on a multi-domain dialog dataset show that the dialog policy guided by the learned reward function achieves remarkably higher task success than state-of-the-art baselines.

Includes members:
  Member "convlab multi domain end to end dialog system platform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "adversarial learning of task oriented neural dialog models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "user modeling for task oriented dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "decoupling strategy and generation in negotiation dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "sentiment adaptive end to end dialog systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory")
  Member "discriminative deep dyna q robust planning for dialogue policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "composite task completion dialogue policy learning via hierarchical deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "emptransfo a multi head transformer architecture for creating empathetic dialog systems")
  Member "learning robust rewards with adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")

Question: Please tell me which class the source paper 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)