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: "a survey and critique of multiagent deep reinforcement learning"
  Abstract: Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"
    - "learning policy representations in multiagent systems"

Connected through hyperedge HE-2 (induced by source paper "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory"):
  with members:
    - "single deep counterfactual regret minimization"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "bounds for approximate regret matching algorithms"
    - "combining no regret and q learning"
    - "a generalized training approach for multiagent learning"
    - "stable predictive optimistic counterfactual regret minimization"
    - "multiagent evaluation under incomplete information"
    - "multi agent adversarial inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "other play for zero shot coordination"):
  with members:
    - "adversarially guided self play for adopting social conventions"
    - "plannable approximations to mdp homomorphisms equivariance under actions"
    - "the hanabi challenge a new frontier for ai research"
    - "improving policies via search in cooperative partially observable games"
    - "on the utility of learning about humans for human ai coordination"
    - "to populate is to regulate"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "theory of minds understanding behavior in groups through inverse planning"

Connected through hyperedge HE-4 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "on applications of bootstrap in continuous space reinforcement learning"
    - "on optimality of adaptive linear quadratic regulators"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "learning unknown markov decision processes a thompson sampling approach"

Connected through hyperedge HE-5 (induced by source paper "multi agent reinforcement learning as a computational tool for language evolution research historical context and future challenges"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "curious intrinsically motivated multi task multi goal reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-6 (induced by source paper "real world human robot collaborative reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "human visual attention prediction boosts learning performance of autonomous driving agents"
    - "pytorch an imperative style high performance deep learning library"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shared autonomy via deep reinforcement learning"
    - "machine theory of mind"
    - "learning to walk via deep reinforcement learning"

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"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "deep reinforcement learning for swarm systems"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"
    - "mean field multi agent reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning when to transfer among agents an efficient multiagent transfer learning framework"):
  with members:
    - "learning transferable cooperative behavior in multi agent teams"
    - "learning to teach in cooperative multiagent reinforcement learning"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"
    - "asynchronous methods for deep 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.MA(Multiagent Systems)