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: "optimal pruning for multi step sensor scheduling"
  Abstract: In the considered linear Gaussian sensor scheduling problem, only one sensor out of a set of sensors performs a measurement. To minimize the estimation error over multiple time steps in a computationally tractable fashion, the so-called information-based pruning algorithm is proposed. It utilizes the information matrices of the sensors and the monotonicity of the Riccati equation. This allows ordering sensors according to their information contribution and excluding many of them from scheduling. Additionally, a tight lower is calculated for branch-and-bound search, which further improves the pruning performance.

Connected through hyperedge HE-1 (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"
    - "design of state based schedulers for a network of control loops"
    - "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-2 (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"
    - "design of state based schedulers for a network of control loops"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

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)