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: "simultaneous state and parameter estimation for second order nonlinear systems"
  Abstract: In this paper, a concurrent learning based adaptive observer is developed for a class of second-order nonlinear time-invariant systems with uncertain dynamics. The developed technique results in uniformly ultimately bounded state and parameter estimation errors. As opposed to persistent excitation which is required for parameter convergence in traditional adaptive control methods, the developed technique only requires excitation over a finite time interval to achieve parameter convergence. Simulation results in both noise-free and noisy environments are presented to validate the design.

Connected through hyperedge HE-1 (induced by source paper "output feedback online optimal control for a class of nonlinear systems"):
  with members:
    - "model based reinforcement learning in differential graphical games"
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "efficient model based reinforcement learning for approximate online optimal control"
    - "model based reinforcement learning for approximate optimal regulation"
    - "learning continuous control policies by stochastic value gradients"
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"

Connected through hyperedge HE-2 (induced by source paper "online inverse reinforcement learning for nonlinear systems"):
  with members:
    - "maximum entropy deep inverse reinforcement learning"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"

Connected through hyperedge HE-3 (induced by source paper "observer side parameter estimation for adaptive control"):
  with members:
    - "integral concurrent learning adaptive control with parameter convergence without pe or state derivatives"
    - "a contraction theory based analysis of the stability of the extended kalman filter"

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.SY(Systems and Control)