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: "moving horizon dynamic power system state estimation using semidefinite relaxation"
  Abstract: Accurate power system state estimation (PSSE) is an essential prerequisite for reliable operation of power systems. Different from static PSSE, dynamic PSSE can exploit past measurements based on a dynamical state evolution model, offering improved accuracy and state predictability. A key challenge is the nonlinear measurement model, which is often tackled using linearization, despite divergence and local optimality issues. In this work, a moving-horizon estimation (MHE) strategy is advocated, where model nonlinearity can be accurately captured with strong performance guarantees. To mitigate local optimality, a semidefinite relaxation approach is adopted, which often provides solutions close to the global optimum. Numerical tests show that the proposed method can markedly improve upon an extended Kalman filter (EKF)-based alternative.

Connected through hyperedge HE-1 (induced by source paper "real time power system state estimation and forecasting via deep unrolled neural networks"):
  with members:
    - "a survey on state estimation techniques and challenges in smart distribution systems"
    - "a learning to infer method for real time power grid multi line outage identification"
    - "learning to optimize training deep neural networks for interference management"
    - "power system state estimation via feasible point pursuit algorithms and cramer rao bound"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "robust and scalable power system state estimation via composite optimization"
    - "psse redux convex relaxation decentralized robust and dynamic approaches"
    - "a learning to infer method for real time power grid topology identification"

Connected through hyperedge HE-2 (induced by source paper "power system state estimation via feasible point pursuit algorithms and cramer rao bound"):
  with members:
    - "ergodic energy management leveraging resource variability in distribution grids"

Connected through hyperedge HE-3 (induced by source paper "robust and scalable power system state estimation via composite optimization"):
  with members:
    - "power system state estimation via feasible point pursuit algorithms and cramer rao bound"
    - "psse redux convex relaxation decentralized robust and dynamic approaches"

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)