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: "autonomous uav navigation using reinforcement learning"
  Abstract: Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research using a UAV with learning capabilities in more important applications, such as wildfire monitoring, or search and rescue missions.

Connected through hyperedge HE-1 (induced by source paper "communication efficient massive uav online path control federated learning meets mean field game theory"):
  with members:
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "remote uav online path planning via neural network based opportunistic control"
    - "reinforcement learning for uav attitude control"
    - "massive uav to ground communication and its stable movement control a mean field approach"
    - "virtualized control over fog interplay between reliability and latency"
    - "energy efficient uav communication with trajectory optimization"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning using genetic algorithm for parameter optimization"):
  with members:
    - "cooperative and distributed reinforcement learning of drones for field coverage"
    - "deep learning with experience ranking convolutional neural network for robot manipulator"
    - "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-3 (induced by source paper "remote uav online path planning via neural network based opportunistic control"):
  with members:
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "wireless network intelligence at the edge"
    - "massive uav to ground communication and its stable movement control a mean field approach"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-4 (induced by source paper "cooperative and distributed reinforcement learning of drones for field coverage"):
  with members:
    - "a distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking"
    - "multi robot transfer learning a dynamical system perspective"
    - "safe multi agent reinforcement learning for autonomous driving"

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)