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: "distributed event based state estimation for networked systems an lmi approach"
  Abstract: In this paper, a dynamic system is controlled by multiple sensor-actuator agents, each of them commanding and observing parts of the system's input and output. The different agents sporadically exchange data with each other via a common bus network according to local event-triggering protocols. From these data, each agent estimates the complete dynamic state of the system and uses its estimate for feedback control. We propose a synthesis procedure for designing the agents’ state estimators and the event triggering thresholds. The resulting distributed and event-based control system is guaranteed to be stable and to satisfy a predefined estimation performance criterion. The approach is applied to the control of a vehicle platoon, where the method's tradeoff between performance and communication and the scalability in the number of agents are demonstrated.

Connected through hyperedge HE-1 (induced by source paper "resource aware iot control saving communication through predictive triggering"):
  with members:
    - "feedback control goes wireless guaranteed stability over low power multi hop networks"
    - "event based state estimation an emulation based approach"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "predictive and self triggering for event based state estimation"
    - "sensor scheduling in variance based event triggered estimation with packet drops"
    - "cyber physical control of road freight transport"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-2 (induced by source paper "event triggered distributed bayes filter"):
  with members:
    - "consensus innovations distributed kalman filter with optimized gains"
    - "stochastic event triggered sensor scheduling for remote state estimation"

Connected through hyperedge HE-3 (induced by source paper "event triggered learning for resource efficient networked control"):
  with members:
    - "event based state estimation an emulation based approach"

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)