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: "probabilistic recursive reasoning for multi agent reinforcement learning"
  Abstract: Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential consequences of others' behaviors so as to plan ahead. It is known that humans use such reasoning ability recursively by considering what others believe about their own beliefs. In this paper, we start from level-$1$ recursion and introduce a probabilistic recursive reasoning (PR2) framework for multi-agent reinforcement learning. Our hypothesis is that it is beneficial for each agent to account for how the opponents would react to its future behaviors. Under the PR2 framework, we adopt variational Bayes methods to approximate the opponents' conditional policies, to which each agent finds the best response and then improve their own policies. We develop decentralized-training-decentralized-execution algorithms, namely PR2-Q and PR2-Actor-Critic, that are proved to converge in the self-play scenarios when there exists one Nash equilibrium. Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge. Our experiments show that it is critical to reason about how the opponents believe about what the agent believes. We expect our work to contribute a new idea of modeling the opponents to the multi-agent reinforcement learning community.

Connected through hyperedge HE-1 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi agent reinforcement learning with emergent roles"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "emergent tool use from multi agent autocurricula"
    - "influence based multi agent exploration"
    - "learning fairness in multi agent systems"
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "maven multi agent variational exploration"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"
    - "multi agent imitation learning for driving simulation"

Connected through hyperedge HE-4 (induced by source paper "learning hierarchical teaching policies for cooperative agents"):
  with members:
    - "teaching on a budget in multi agent deep reinforcement learning"
    - "learning abstract options"
    - "learning to teach in cooperative multiagent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "hierarchical imitation and reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "counterfactual multi agent policy gradients"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "influence based multi agent exploration"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "coordinated exploration via intrinsic rewards for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "exploration by random network distillation"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "information maximizing exploration with a latent dynamics model"

Connected through hyperedge HE-6 (induced by source paper "learning nearly decomposable value functions via communication minimization"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "agent probing interaction policies"):
  with members:
    - "environment probing interaction policies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "vain attentional multi agent predictive modeling"

Connected through hyperedge HE-8 (induced by source paper "a regularized opponent model with maximum entropy objective"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "machine theory of mind"
    - "balancing two player stochastic games with soft q 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)