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 "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  Abstract: Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt to solve this challenge in the context of decentralized learning in multi-agent linear-quadratic (LQ) dynamical systems. We begin with a simple setup consisting of two agents and two dynamically decoupled stochastic linear systems, each system controlled by an agent. The systems are coupled through a quadratic cost function. When both systems' dynamics are unknown and there is no communication among the agents, we show that no learning policy can generate sub-linear in $T$ regret, where $T$ is the time horizon. When only one system's dynamics are unknown and there is one-directional communication from the agent controlling the unknown system to the other agent, we propose a MARL algorithm based on the construction of an auxiliary single-agent LQ problem. The auxiliary single-agent problem in the proposed MARL algorithm serves as an implicit coordination mechanism among the two learning agents. This allows the agents to achieve a regret within $O(\sqrt{T})$ of the regret of the auxiliary single-agent problem. Consequently, using existing results for single-agent LQ regret, our algorithm provides a $\tilde{O}(\sqrt{T})$ regret bound. (Here $\tilde{O}(\cdot)$ hides constants and logarithmic factors). Our numerical experiments indicate that this bound is matched in practice. From the two-agent problem, we extend our results to multi-agent LQ systems with certain communication patterns.

Includes members:
  Member "the hanabi challenge a new frontier for ai research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "other play for zero shot coordination")
  Member "a survey and critique of multiagent deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "on applications of bootstrap in continuous space reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "on optimality of adaptive linear quadratic regulators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "logarithmic regret for adversarial online control")
  Member "multi agent reinforcement learning via double averaging primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "behavior planning for connected autonomous vehicles using feedback deep reinforcement learning")
  Member "fully decentralized multi agent reinforcement learning with networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cluster based social reinforcement learning")
  Member "finite time analysis of optimal adaptive policies for linear quadratic systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "least squares temporal difference learning for the linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "naive exploration is optimal for online lqr")

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.LG(Machine Learning)