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 "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"):
  Abstract: Creating an intelligent conversational system that understands vision and language is one of the ultimate goals in Artificial Intelligence (AI)~\cite{winograd1972understanding}. Extensive research has focused on vision-to-language generation, however, limited research has touched on combining these two modalities in a goal-driven dialog context. We propose a multimodal hierarchical reinforcement learning framework that dynamically integrates vision and language for task-oriented visual dialog. The framework jointly learns the multimodal dialog state representation and the hierarchical dialog policy to improve both dialog task success and efficiency. We also propose a new technique, state adaptation, to integrate context awareness in the dialog state representation. We evaluate the proposed framework and the state adaptation technique in an image guessing game and achieve promising results.

Includes members:
  Member "feudal reinforcement learning for dialogue management in large domains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ensemble based deep reinforcement learning for chatbots")
  Member "evaluating visual conversational agents via cooperative human ai games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep bayesian network for visual question generation")
  Member "learning conversational systems that interleave task and non task content":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "if i hear you correctly building and evaluating interview chatbots with active listening skills")
  Member "asking the difficult questions goal oriented visual question generation via intermediate rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient")
  Member "image grounded conversations multimodal context for natural question and response generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog")
  Member "sub domain modelling for dialogue management with hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a multimodal dialogue system for conversational image editing")

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)