human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "output feedback online optimal control for a class of nonlinear systems"):
  Abstract: In this paper an output-feedback model-based reinforcement learning (MBRL) method for a class of second-order nonlinear systems is developed. The control technique uses exact model knowledge and integrates a dynamic state estimator within the model-based reinforcement learning framework to achieve output-feedback MBRL. Simulation results demonstrate the efficacy of the developed method.

Includes members:
  Member "model based reinforcement learning in differential graphical games":
    also connected through other hyperedges:
      (none)
  Member "model based reinforcement learning for infinite horizon approximate optimal tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi pseudo q learning based deterministic policy gradient for tracking control of autonomous underwater vehicles")
  Member "simultaneous state and parameter estimation for second order nonlinear systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online inverse reinforcement learning for nonlinear systems")
  Member "efficient model based reinforcement learning for approximate online optimal control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "verification for machine learning autonomy and neural networks survey")
  Member "model based reinforcement learning for approximate optimal regulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "model based reinforcement learning in differential graphical games")
  Member "learning continuous control policies by stochastic value gradients":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "approximate optimal trajectory tracking for continuous time nonlinear systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "verification for machine learning autonomy and neural networks survey")
  Member "gaussian processes for sample efficient reinforcement learning with rmax like exploration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "context dependent upper confidence bounds for directed exploration")

Question: Please tell me which class the source paper 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.SY(Systems and Control)