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: "revisiting the master slave architecture in multi agent deep reinforcement learning"
  Abstract: Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take the form of two seemingly conflicting perspectives, the decentralized perspective, where each agent is supposed to have its own controller; and the centralized perspective, where one assumes there is a larger model controlling all agents. In this regard, we revisit the idea of the master-slave architecture by incorporating both perspectives within one framework. Such a hierarchical structure naturally leverages advantages from one another. The idea of combining both perspective is intuitive and can be well motivated from many real world systems, however, out of a variety of possible realizations, we highlights three key ingredients, i.e. composed action representation, learnable communication and independent reasoning. With network designs to facilitate these explicitly, our proposal consistently outperforms latest competing methods both in synthetics experiments and when applied to challenging StarCraft micromanagement tasks.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "r maddpg for partially observable environments and limited communication"):
  with members:
    - "emergent coordination through competition"
    - "learning attentional communication for multi agent cooperation"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"
    - "counterfactual multi agent policy gradients"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"

Connected through hyperedge HE-3 (induced by source paper "learning agent communication under limited bandwidth by message pruning"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "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"
    - "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-4 (induced by source paper "feudal multi agent hierarchies for cooperative reinforcement learning"):
  with members:
    - "hierarchical deep multiagent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "feudal networks for hierarchical reinforcement learning"
    - "federated control with hierarchical multi agent deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent deep reinforcement learning with human strategies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "a human mixed strategy approach to deep reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "cooperative multi agent policy gradients with sub optimal demonstration"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "deep q learning from demonstrations"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Connected through hyperedge HE-7 (induced by source paper "learning attentional communication for multi agent cooperation"):
  with members:
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "emergence of grounded compositional language in multi agent populations"
    - "coordinated multi agent imitation learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "learning multiagent communication with backpropagation"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-8 (induced by source paper "macro action selection with deep reinforcement learning in starcraft"):
  with members:
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "distributed prioritized experience replay"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "deep recurrent q learning for partially observable mdps"
    - "a dataset for starcraft ai an example of armies clustering"
    - "inferring strategies from limited reconnaissance in real time strategy games"

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)