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 "learning reinforced agents with counterfactual simulation for medical automatic diagnosis"):
  Abstract: Medical automatic diagnosis (MAD) aims to learn an agent that mimics the behavior of a human doctor, i.e. inquiring symptoms and informing diseases. Due to medical ethics concerns, it is impractical to directly apply reinforcement learning techniques to solving MAD, e.g., training a reinforced agent with the human patient. Developing a patient simulator by using the collected patient-doctor dialogue records has been proposed as a promising approach to MAD. However, most of these existing works overlook the causal relationship between patient symptoms and disease diagnoses. For example, these simulators simply generate the ``not-sure'' response to the inquiry (i.e., symptom) that was not observed in one dialogue record. As a result, the MAD agent is usually trained without exploiting the counterfactual reasoning beyond the factual observations. To address this problem, this paper presents a propensity-based patient simulator (PBPS), which is capable of facilitating the training of MAD agents by generating informative counterfactual answers along with the disease diagnosis. Specifically, our PBPS estimates the propensity score of each record with the patient-doctor dialogue reasoning, and can thus generate the counterfactual answers by searching across records. That is, the unrecorded symptom for one patient can be found in the records of other patients according to the propensity score matching. A progressive assurance agent (P2A) can be thus trained with PBPS, which includes two separate yet cooperative branches accounting for the execution of symptom-inquiry and disease-diagnosis actions, respectively. The disease-diagnosis predicts the confidence of disease and drives the symptom-inquiry in terms of enhancing the confidence, and the two branches are jointly optimized with benefiting from each other.

Includes members:
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "theoretical impediments to machine learning with seven sparks from the causal revolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "integrating planning for task completion dialogue policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "robustness to out of distribution inputs via task aware generative uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time out of distribution detection in learning enabled cyber physical systems")
  Member "a survey on dialogue systems recent advances and new frontiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "continuous melody generation via disentangled short term representations and structural conditions")
  Member "towards end to end reinforcement learning of dialogue agents for information access":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "a user simulator for task completion dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised dialogue learning")
  Member "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural contextual bandits with ucb based exploration")

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.CV(Computer Vision and Pattern Recognition)