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 "learn task first or learn human partner first deep reinforcement learning of human robot cooperation in asymmetric hierarchical dynamic task"):
  Abstract: The deep reinforcement learning method for human-robot cooperation (HRC) is promising for its high performance when robots are learning complex tasks. However, the applicability of such an approach in a real-world context is limited due to long training time, additional training difficulty caused by inconsistent human performance and the inherent instability of policy exploration. With this approach, the robot has two dynamics to learn: how to accomplish the given physical task and how to cooperate with the human partner. Furthermore, the dynamics of the task and human partner are usually coupled, which means the observable outcomes and behaviors are coupled. It is hard for the robot to efficiently learn from coupled observations. In this paper, we hypothesize that the robot needs to learn the task separately from learning the behavior of the human partner to improve learning efficiency and outcomes. This leads to a fundamental question: Should the robot learn the task first or learn the human behavior first (Fig. 1)? We develop a novel hierarchical rewards mechanism with a task decomposition method that enables the robot to efficiently learn a complex hierarchical dynamic task and human behavior for better HRC. The algorithm is validated in a hierarchical control task in a simulated environment with human subject experiments, and we are able to answer the question by analyzing the collected experiment results.

Includes members:
  Member "shared autonomy via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real world human robot collaborative reinforcement learning")
  Member "human robot mutual adaptation in shared autonomy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling")
  Member "shared autonomy via hindsight optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections")
  Member "reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

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.RO(Robotics)