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: "psse redux convex relaxation decentralized robust and dynamic approaches"
  Abstract: This chapter aspires to glean some of the recent advances in power system state estimation (PSSE), though our collection is not exhaustive by any means. The Cram{e}r-Rao bound, a lower bound on the (co)variance of any unbiased estimator, is first derived for the PSSE setup. After reviewing the classical Gauss-Newton iterations, contemporary PSSE solvers leveraging relaxations to convex programs and successive convex approximations are explored. A disciplined paradigm for distributed and decentralized schemes is subsequently exemplified under linear(ized) and exact grid models. Novel bad data processing models and fresh perspectives linking critical measurements to cyber-attacks on the state estimator are presented. Finally, spurred by advances in online convex optimization, model-free and model-based state trackers are reviewed.

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"
    - "a learning to infer method for real time power grid topology identification"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "canonical correlation analysis of datasets with a common source graph"):
  with members:
    - "nonlinear dimensionality reduction for discriminative analytics of multiple datasets"
    - "nonlinear dimensionality reduction on graphs"
    - "dpca dimensionality reduction for discriminative analytics of multiple large scale datasets"
    - "fast robust pca on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "blind identification of simo wiener systems based on kernel canonical correlation analysis"

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"
    - "moving horizon dynamic power system state estimation using semidefinite relaxation"

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)