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 multi agent reinforcement learning model of common pool resource appropriation"
  Abstract: Humanity faces numerous problems of common-pool resource appropriation. This class of multi-agent social dilemma includes the problems of ensuring sustainable use of fresh water, common fisheries, grazing pastures, and irrigation systems. Abstract models of common-pool resource appropriation based on non-cooperative game theory predict that self-interested agents will generally fail to find socially positive equilibria---a phenomenon called the tragedy of the commons. However, in reality, human societies are sometimes able to discover and implement stable cooperative solutions. Decades of behavioral game theory research have sought to uncover aspects of human behavior that make this possible. Most of that work was based on laboratory experiments where participants only make a single choice: how much to appropriate. Recognizing the importance of spatial and temporal resource dynamics, a recent trend has been toward experiments in more complex real-time video game-like environments. However, standard methods of non-cooperative game theory can no longer be used to generate predictions for this case. Here we show that deep reinforcement learning can be used instead. To that end, we study the emergent behavior of groups of independently learning agents in a partially observed Markov game modeling common-pool resource appropriation. Our experiments highlight the importance of trial-and-error learning in common-pool resource appropriation and shed light on the relationship between exclusion, sustainability, and inequality.

Connected through hyperedge HE-1 (induced by source paper "cluster based social reinforcement learning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "emergent coordination through competition"
    - "personalization of health interventions using cluster based reinforcement learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "m 3 rl mind aware multi agent management reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "open ended learning in symmetric zero sum games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "social diversity and social preferences in mixed motive reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "on the utility of learning about humans for human ai coordination"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"

Connected through hyperedge HE-4 (induced by source paper "silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "legible normativity for ai alignment the value of silly rules"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"
    - "representation learning with contrastive predictive coding"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"

Connected through hyperedge HE-5 (induced by source paper "inducing cooperative behaviour in sequential social dilemmas through multi agent reinforcement learning using status quo loss"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "emergent tool use from multi agent autocurricula"):
  with members:
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "structured agents for physical construction"
    - "exploration by random network distillation"

Connected through hyperedge HE-7 (induced by source paper "evolutionary reinforcement learning for sample efficient multiagent coordination"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "emergent coordination through competition"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "pommerman a multi agent playground"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-8 (induced by source paper "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "a generalised method for empirical game theoretic analysis"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "episodic curiosity through reachability"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"

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)