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 "building task oriented visual dialog systems through alternative optimization between dialog policy and language generation"):
  Abstract: Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. A common practice is to apply RL on a neural sequence-to-sequence (seq2seq) framework with the action space being the output vocabulary in the decoder. However, it is difficult to design a reward function that can achieve a balance between learning an effective policy and generating a natural dialog response. This paper proposes a novel framework that alternatively trains a RL policy for image guessing and a supervised seq2seq model to improve dialog generation quality. We evaluate our framework on the GuessWhich task and the framework achieves the state-of-the-art performance in both task completion and dialog quality.

Includes members:
  Member "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems")
  Member "multimodal hierarchical reinforcement learning policy for task oriented visual dialog":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical reinforcement learning for open domain dialog")
  Member "sentiment adaptive end to end dialog systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory")
  Member "dialog based interactive image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog")
  Member "evaluating visual conversational agents via cooperative human ai games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep bayesian network for visual question generation")
  Member "asking the difficult questions goal oriented visual question generation via intermediate rewards":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "deal or no deal end to end learning for negotiation dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recent advances and challenges in task oriented dialog system")

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)