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: "cooperative and distributed reinforcement learning of drones for field coverage"
  Abstract: This paper proposed a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for a team of Unmanned Aerial Vehicles (UAVs) that can learn to cooperate to provide a full coverage of an unknown field of interest while minimizing the overlapping sections among their field of views. Two challenges in MARL for such a system are discussed in the paper: firstly, the complex dynamic of the joint-actions of the UAV team, that will be solved using game-theoretic correlated equilibrium, and secondly, the challenge in huge dimensional state space will be tackled with an efficient space-reduced representation of the value function table. We also provided our experimental results in details with both simulation and physical implementation to show that the UAVs can successfully learn to accomplish the task without the need of a mathematical model. This paper can serve as an efficient framework for using MARL to enable UAV team to work in an environment where its model is unavailable. In the future, we are interested in accounting for the stochastic aspect of the problem and technical aspects of a real-world deployment, where uncertainties, such as wind and other dynamics of the environment presents.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning using genetic algorithm for parameter optimization"):
  with members:
    - "deep learning with experience ranking convolutional neural network for robot manipulator"
    - "autonomous uav navigation using reinforcement learning"
    - "a comparison of various approaches to reinforcement learning algorithms for multi robot box pushing"
    - "hindsight experience replay"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi agent common knowledge reinforcement learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "multi vehicle flocking control with deep deterministic policy gradient method"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "learning for multi robot cooperation in partially observable stochastic environments with macro actions"

Connected through hyperedge HE-3 (induced by source paper "multi vehicle flocking control with deep deterministic policy gradient method"):
  with members:
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "adam a method for stochastic optimization"

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.RO(Robotics)