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: "design of state based schedulers for a network of control loops"
  Abstract: For a closed-loop system with a contention-based multiple access network on its sensor link, the medium access controller (MAC) may discard some packets when the traffic on the link is high. We use a local state-based scheduler to select a few critical data packets to send to the MAC. In this paper, we analyze the impact of such a scheduler on the closed-loop system in the presence of traffic, and show that there is a dual effect with state-based scheduling. In general, this makes the optimal scheduler and controller hard to find. However, by removing past controls from the scheduling criterion, we find that certainty equivalence holds. This condition is related to the classical result of Bar-Shalom and Tse, and it leads to the design of an innovations-based scheduler with a certainty equivalent controller. However, this controller is not an equivalent design for the optimal controller, in the sense of Witsenhausen. The computation of the estimate can be simplified by introducing a symmetry constraint on the scheduler. Based on these findings, we propose a dual predictor architecture for the closed-loop system, which ensures separation between scheduler, observer and controller. We present an example of this architecture, which illustrates a network-aware event-triggering mechanism.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for scheduling in large scale networked control systems"):
  with members:
    - "deepcas a deep reinforcement learning algorithm for control aware scheduling"
    - "deep reinforcement learning for wireless sensor scheduling in cyber physical systems"
    - "deep reinforcement learning for event triggered control"
    - "wireless network design for control systems a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deepcas a deep reinforcement learning algorithm for control aware scheduling"):
  with members:
    - "trade offs in stochastic event triggered control"
    - "wireless network design for control systems a survey"
    - "multiple loop self triggered model predictive control for network scheduling and control"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "infinite horizon optimal transmission power control for remote state estimation over fading channels"):
  with members:
    - "multi sensor scheduling for state estimation with event based stochastic triggers"
    - "on the existence of optimal policies for a class of static and sequential dynamic teams"
    - "distortion transmission trade off in real time transmission of markov sources"
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "optimal pruning for multi step sensor scheduling"
    - "discretized approximations for pomdp with average cost"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"
    - "paging and registration in cellular networks jointly optimal policies and an iterative algorithm"

Connected through hyperedge HE-4 (induced by source paper "sensor scheduling in variance based event triggered estimation with packet drops"):
  with members:
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "optimal pruning for multi step sensor scheduling"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

Connected through hyperedge HE-5 (induced by source paper "multi sensor scheduling for state estimation with event based stochastic triggers"):
  with members:
    - "stochastic event triggered sensor scheduling for remote state estimation"

Connected through hyperedge HE-6 (induced by source paper "optimal triggering of networked control systems"):
  with members:
    - "lambda policy iteration a review and a new implementation"
    - "stabilizing value iteration with and without approximation errors"
    - "theoretical and numerical analysis of approximate dynamic programming with approximation errors"

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)