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: "multiple loop self triggered model predictive control for network scheduling and control"
  Abstract: We present an algorithm for controlling and scheduling multiple linear time-invariant processes on a shared bandwidth-limited communication network using adaptive sampling intervals. The controller is centralized and not only computes at every sampling instant the new control command for a process but also decides the time interval to wait until taking the next sample. The approach relies on model predictive control ideas, where the cost function penalizes the state and control effort as well as the time interval until the next sample is taken. The latter is introduced to generate an adaptive sampling scheme for the overall system such that the sampling time increases as the norm of the system state goes to zero. This paper presents a method for synthesizing such a predictive controller and gives explicit sufficient conditions for when it is stabilizing. Further explicit conditions are given that guarantee conflict free transmissions on the network. It is shown that the optimization problem may be solved offline and that the controller can be implemented as a lookup table of state feedback gains. The simulation studies which compare the proposed algorithm to periodic sampling illustrate potential performance gains.

Connected through hyperedge HE-1 (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"
    - "adam a method for stochastic optimization"
    - "design of state based schedulers for a network of control loops"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "wireless network design for control systems a survey"):
  with members:
    - "optimal power control and rate adaptation for ultra reliable m2m control applications"
    - "end to end reliability aware scheduling for wireless sensor networks"
    - "performance analysis of a network of event based systems"

Connected through hyperedge HE-3 (induced by source paper "optimal control of linear systems with limited control actions threshold based event triggered control"):
  with members:
    - "sparse packetized predictive control for networked control over erasure channels"
    - "an improved three weight message passing algorithm"
    - "sparse representations for packetized predictive networked control"

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)