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: "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"
  Abstract: Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent "learning-to-communicate" studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which can not generalize to changing environment or large collection of agents. #R##N#In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving "learning-to-communicate" problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can efficiently learn the communication protocols even from scratch under partially observable environment. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations.

Connected through hyperedge HE-1 (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"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Connected through hyperedge HE-2 (induced by source paper "learning multi agent communication under limited bandwidth restriction for internet packet routing"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "a survey of learning in multiagent environments dealing with non stationarity"

Connected through hyperedge HE-3 (induced by source paper "stigmergic independent reinforcement learning for multi agent collaboration"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "stigmergy based modeling to discover urban activity patterns from positioning data"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "prioritized experience replay"
    - "mobile crowd sensing and computing when participatory sensing meets participatory social media"
    - "the arcade learning environment an evaluation platform for general agents"
    - "antnet distributed stigmergetic control for communications networks"

Connected through hyperedge HE-4 (induced by source paper "reward design in cooperative multi agent reinforcement learning for packet routing"):
  with members:
    - "learning agent communication under limited bandwidth by message pruning"
    - "neighborhood cognition consistent multi agent reinforcement learning"
    - "learning multi agent communication under limited bandwidth restriction for internet packet routing"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "counterfactual multi agent policy gradients"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "analysing congestion problems in multi agent reinforcement learning"
    - "reinforcement mechanism design for e commerce"

Connected through hyperedge HE-5 (induced by source paper "modelling the dynamic joint policy of teammates with attention multi agent ddpg"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "mean field multi agent reinforcement learning"
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "revisiting the master slave architecture in multi agent deep reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "collaborative deep reinforcement learning for joint object search"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep reinforcement learning an overview"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-7 (induced by source paper "learning to communicate a machine learning framework for heterogeneous multi agent robotic systems"):
  with members:
    - "unity a general platform for intelligent agents"
    - "hidden markov model estimation based q learning for partially observable markov decision process"
    - "counterfactual multi agent policy gradients"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "memory based control with recurrent neural networks"

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)