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 "resource aware iot control saving communication through predictive triggering"):
  Abstract: The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the “things” coordinate decisions and act collectively on shared information,  feedback  is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: 1) self triggers that predict, at the current triggering instant, the next one and 2) predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multivehicle simulation.

Includes members:
  Member "feedback control goes wireless guaranteed stability over low power multi hop networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "synchronous transmissions in low power wireless a survey of communication protocols and network services")
  Member "distributed event based state estimation for networked systems an lmi approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "event triggered distributed bayes filter")
  Member "event based state estimation an emulation based approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "event triggered learning")
  Member "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cyborg an autonomous cyber operations research gym")
  Member "predictive and self triggering for event based state estimation":
    also connected through other hyperedges:
      (none)
  Member "sensor scheduling in variance based event triggered estimation with packet drops":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stabilizing scheduling policies for networked control systems")
  Member "cyber physical control of road freight transport":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of a stochastic model for coordinated platooning of heavy duty vehicles")
  Member "internet of things iot a vision architectural elements and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neuromemristive circuits for edge computing a review")

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)