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 "context based concurrent experience sharing in multiagent systems"):
  Abstract: One of the key challenges for multi-agent learning is scalability. In this paper, we introduce a technique for speeding up multi-agent learning by exploiting concurrent and incremental experience sharing. This solution adaptively identifies opportunities to transfer experiences between agents and allows for the rapid acquisition of appropriate policies in large-scale, stochastic, homogeneous multi-agent systems. We introduce an online, distributed, supervisor-directed transfer technique for constructing high-level characterizations of an agent's dynamic learning environment---called contexts---which are used to identify groups of agents operating under approximately similar dynamics within a short temporal window. A set of supervisory agents computes contextual information for groups of subordinate agents, thereby identifying candidates for experience sharing. Our method uses a tiered architecture to propagate, with low communication overhead, state, action, and reward data amongst the members of each dynamically-identified information-sharing group. We applied this method to a large-scale distributed task allocation problem with hundreds of information-sharing agents operating in an unknown, non-stationary environment. We demonstrate that our approach results in significant performance gains, that it is robust to noise-corrupted or suboptimal context features, and that communication costs scale linearly with the supervisor-to-subordinate ratio.

Includes members:
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "accelerating reinforcement learning through implicit imitation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ensembledagger a bayesian approach to safe imitation learning")
  Member "state abstraction in maxq hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical linearly solvable markov decision problems")

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.MA(Multiagent Systems)