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 schedule communication in multi agent reinforcement learning"
  Abstract: Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable multiple agents to communicate with each other in a distributed manner and behave as a group. In this paper, we study a practical scenario when (i) the communication bandwidth is limited and (ii) the agents share the communication medium so that only a restricted number of agents are able to simultaneously use the medium, as in the state-of-the-art wireless networking standards. This calls for a certain form of communication scheduling. In that regard, we propose a multi-agent deep reinforcement learning framework, called SchedNet, in which agents learn how to schedule themselves, how to encode the messages, and how to select actions based on received messages. SchedNet is capable of deciding which agents should be entitled to broadcasting their (encoded) messages, by learning the importance of each agent's partially observed information. We evaluate SchedNet against multiple baselines under two different applications, namely, cooperative communication and navigation, and predator-prey. Our experiments show a non-negligible performance gap between SchedNet and other mechanisms such as the ones without communication and with vanilla scheduling methods, e.g., round robin, ranging from 32% to 43%.

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:
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "probabilistic recursive reasoning for multi agent 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:
    - "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"
    - "probabilistic recursive reasoning for 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 "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs"):
  with members:
    - "learning nearly decomposable value functions via communication minimization"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "learning transferable cooperative behavior in multi agent teams"
    - "learning attentional communication for multi agent cooperation"
    - "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"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (induced by source paper "pic permutation invariant critic for multi agent deep reinforcement learning"):
  with members:
    - "factor graph attention"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "link prediction based on graph neural networks"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "learning nearly decomposable value functions via communication minimization"):
  with members:
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for 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-6 (induced by source paper "efficient communication in multi agent reinforcement learning via variance based control"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "multi agent common knowledge reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "value decomposition networks for cooperative multi agent learning"

Connected through hyperedge HE-7 (induced by source paper "learning agent communication under limited bandwidth by message pruning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"
    - "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Connected through hyperedge HE-8 (induced by source paper "learning structured communication for multi agent reinforcement learning"):
  with members:
    - "multi agent actor critic with hierarchical graph attention network"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "graph convolutional reinforcement learning"
    - "actor attention critic for 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.AI(Artificial Intelligence)