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: "learning to teach in cooperative multiagent reinforcement learning"
  Abstract: Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in distributed learning systems would likely benefit from communication to share knowledge and teach skills. The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to. This learning to teach problem has inherent complexities related to measuring long-term impacts of teaching that compound the standard multiagent coordination challenges. In contrast to existing works, this paper presents the first general framework and algorithm for intelligent agents to learn to teach in a multiagent environment. Our algorithm, Learning to Coordinate and Teach Reinforcement (LeCTR), addresses peer-to-peer teaching in cooperative multiagent reinforcement learning. Each agent in our approach learns both when and what to advise, then uses the received advice to improve local learning. Importantly, these roles are not fixed; these agents learn to assume the role of student and/or teacher at the appropriate moments, requesting and providing advice in order to improve teamwide performance and learning. Empirical comparisons against state-of-the-art teaching methods show that our teaching agents not only learn significantly faster, but also learn to coordinate in tasks where existing methods fail.

Connected through hyperedge HE-1 (induced by source paper "learning hierarchical teaching policies for cooperative agents"):
  with members:
    - "teaching on a budget in multi agent deep reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "learning abstract options"
    - "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-2 (induced by source paper "transfer heterogeneous knowledge among peer to peer teammates a model distillation approach"):
  with members:
    - "distributional reinforcement learning with linear function approximation"
    - "search to distill pearls are everywhere but not the eyes"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep mutual learning"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "badger learning to learn learning algorithms through multi agent communication"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "efficient optimization of loops and limits with randomized telescoping sums"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning of sequential strategies"
    - "modularity as a means for complexity management in neural networks learning"
    - "a mathematical theory of cooperative communication"
    - "learning to communicate in multi agent reinforcement learning a review"
    - "mastering atari go chess and shogi by planning with a learned model"

Connected through hyperedge HE-5 (induced by source paper "policy distillation and value matching in multiagent reinforcement learning"):
  with members:
    - "learning hierarchical teaching in cooperative multiagent reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "coordinated multi agent imitation learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "reinforcement learning with deep energy based policies"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-6 (induced by source paper "shapley q value a local reward approach to solve global reward games"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning multiagent communication with backpropagation"

Connected through hyperedge HE-7 (induced by source paper "teaching on a budget in multi agent deep reinforcement learning"):
  with members:
    - "learning hierarchical teaching in cooperative multiagent reinforcement learning"
    - "exploration by random network distillation"
    - "learning to teach reinforcement learning agents"
    - "theoretically grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "playing atari with 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)