human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi agent reinforcement learning a selective overview of theories and algorithms"
  Abstract: Recent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature. In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games, in accordance with the types of tasks they address, i.e., fully cooperative, fully competitive, and a mix of the two. We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests. Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.

Connected through hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "emergent tool use from multi agent autocurricula"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"

Connected through hyperedge HE-2 (induced by source paper "provable self play algorithms for competitive reinforcement learning"):
  with members:
    - "reward free exploration for reinforcement learning"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "corruption robust exploration in episodic reinforcement learning"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "feature based q learning for two player stochastic games"
    - "reinforcement learning under drift"

Connected through hyperedge HE-3 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks"):
  with members:
    - "asynchronous decentralized optimization in directed networks"
    - "distributed metropolis sampler with optimal parallelism"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "distributed off policy actor critic reinforcement learning with policy consensus"
    - "asynchronous subgradient push"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "communication efficient distributed reinforcement learning"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-4 (induced by source paper "q learning for mean field controls"):
  with members:
    - "learning to control in metric space with optimal regret"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "model based reinforcement learning for atari"
    - "an algorithmic perspective on imitation learning"
    - "q learning with nearest neighbors"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "practical kernel based reinforcement learning"
    - "efficient reinforcement learning in deterministic systems with value function generalization"

Connected through hyperedge HE-5 (induced by source paper "reward design for driver repositioning using multi agent reinforcement learning"):
  with members:
    - "multi agent reinforcement learning for order dispatching via order vehicle distribution matching"
    - "optimal passenger seeking policies on e hailing platforms using markov decision process and imitation learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "behavior planning for connected autonomous vehicles using feedback deep reinforcement learning"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "control of connected and automated vehicles state of the art and future challenges"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "virtual to real reinforcement learning for autonomous driving"
    - "end to end driving via conditional imitation learning"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "adaptive online distributed optimal control of very large scale robotic systems"):
  with members:
    - "represented value function approach for large scale multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "deep reinforcement learning for swarm systems"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"
    - "mean field multi agent reinforcement learning"

Question: Please tell me which class the center node 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)