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: "a distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking"
  Abstract: Wildland fire fighting is a very dangerous job, and the lack of information of the fire front is one of main reasons that causes many accidents. Using unmanned aerial vehicle (UAV) to cover wildfire is promising because it can replace human in hazardous fire tracking and save operation costs significantly. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collision as well as cooperate well with other neighbors. Experimental results are conducted to demonstrate the capabilites of the UAV team in covering a spreading wildfire.

Connected through hyperedge HE-1 (induced by source paper "safe coordination of human robot firefighting teams"):
  with members:
    - "adaptive adjustment of noise covariance in kalman filter for dynamic state estimation"

Connected through hyperedge HE-2 (induced by source paper "cooperative and distributed reinforcement learning of drones for field coverage"):
  with members:
    - "autonomous uav navigation using reinforcement learning"
    - "multi robot transfer learning a dynamical system perspective"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "a distributed control framework of multiple unmanned aerial vehicles for dynamic wildfire tracking"):
  with members:
    - "development of a swarm uav simulator integrating realistic motion control models for disaster operations"
    - "development of an autonomous bridge deck inspection robotic system"
    - "securing a uav using individual characteristics from an eeg signal"

Connected through hyperedge HE-4 (induced by source paper "distributed formation control for autonomous robots in dynamic environments"):
  with members:
    - "bounded distributed flocking control of nonholonomic mobile robots"
    - "distributed formation control of nonlonolomic mobile robots by bounded feedback in the presence of obstacles"

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)