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: "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
  Abstract: Modelling and exploiting teammates' policies in cooperative multi-agent systems have long been an interest and also a big challenge for the reinforcement learning (RL) community. The interest lies in the fact that if the agent knows the teammates' policies, it can adjust its own policy accordingly to arrive at proper cooperations; while the challenge is that the agents' policies are changing continuously due to they are learning concurrently, which imposes difficulty to model the dynamic policies of teammates accurately. In this paper, we present \emph{ATTention Multi-Agent Deep Deterministic Policy Gradient} (ATT-MADDPG) to address this challenge. ATT-MADDPG extends DDPG, a single-agent actor-critic RL method, with two special designs. First, in order to model the teammates' policies, the agent should get access to the observations and actions of teammates. ATT-MADDPG adopts a centralized critic to collect such information. Second, to model the teammates' policies using the collected information in an effective way, ATT-MADDPG enhances the centralized critic with an attention mechanism. This attention mechanism introduces a special structure to explicitly model the dynamic joint policy of teammates, making sure that the collected information can be processed efficiently. We evaluate ATT-MADDPG on both benchmark tasks and the real-world packet routing tasks. Experimental results show that it not only outperforms the state-of-the-art RL-based methods and rule-based methods by a large margin, but also achieves better performance in terms of scalability and robustness.

Connected through hyperedge HE-1 (induced by source paper "attentional policies for cross context multi agent reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "actor attention critic for multi agent reinforcement learning"
    - "self attention with relative position representations"
    - "relational inductive biases deep learning and graph networks"
    - "a review on cooperative adaptive cruise control cacc systems architectures controls and applications"
    - "flow a modular learning framework for autonomy in traffic"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "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"
    - "a survey of learning in multiagent environments dealing with non stationarity"

Connected through hyperedge HE-4 (induced by source paper "neighborhood cognition consistent multi agent reinforcement learning"):
  with members:
    - "a review of cooperative multi agent deep reinforcement learning"
    - "google research football a novel reinforcement learning environment"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "counterfactual multi agent policy gradients"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "a distributional perspective on 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.LG(Machine Learning)