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 "observer side parameter estimation for adaptive control"):
  Abstract: In adaptive control, a controller is precisely designed for a certain model of the system, but that model's parameters are updated online by another mechanism called the adaptive update. This allows the controller to aim for the benefits of exact model knowledge while simultaneously remaining robust to model uncertainty. #R##N#Like most nonlinear controllers, adaptive controllers are often designed and analyzed under the assumption of deterministic full state feedback. However, doing so inherently decouples the adaptive update mechanism from the probabilistic information provided by modern state observers. #R##N#The simplest way to reconcile this is to let the observer produce both state estimates and model parameter estimates, so that all probabilistic information is shared within the framework of the observer. While this technique is becoming increasingly common, it is still not widely accepted due to a lack of general closed-loop stability proofs. #R##N#In this thesis, we will investigate observer-side parameter estimation for adaptive control by precisely juxtaposing its mechanics against the current, widely accepted adaptive control designs. Additionally, we will propose a new technique that increases the robustness of observer-based adaptive control by following the same line of reasoning used for the popular concurrent learning method.

Includes members:
  Member "simultaneous state and parameter estimation for second order nonlinear systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "output feedback online optimal control for a class of nonlinear systems")
  Member "integral concurrent learning adaptive control with parameter convergence without pe or state derivatives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse wide area control of power systems using data driven reinforcement learning")
  Member "a contraction theory based analysis of the stability of the extended kalman filter":
    also connected through other hyperedges:
      (none)

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)