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: "structural emergency control paradigm"
  Abstract: Power systems normally operate at some stable operating condition where the power supply and demand are balanced. In emergency situations, local protective devices are usually activated to cut a suitable amount of loads to quickly rebalance the supply-demand and hopefully stabilize the system. This traditional emergency control results in interrupted service with severely economic damages to customers. Also, such local control is usually less effective due to the lack of coordination among protective devices and the difference in their timescales. In this paper, we propose a novel structural control paradigm for remedial actions of power grids, which can rigorously guarantee to render the post-fault dynamics from the fault-cleared state to the stable equilibrium point. This is a new control paradigm not relying on any continuous measurement. Instead, the grid is made stable by intentionally changing the power network structure, and thereby discretely changing its equilibrium point and the corresponding region of attraction such that the system is consecutively attracted from the fault-cleared state back to the original equilibrium point. The proposed control is designed by solving convex optimization problems, making it possibly scalable to large scale power grids. Finally, this emergency control scheme can be implemented with minor investment by exploiting the plentiful transmission facilities ubiquitously available on the existing power grids.

Connected through hyperedge HE-1 (induced by source paper "deep learning for power system security assessment"):
  with members:
    - "similarity encoding for learning with dirty categorical variables"
    - "deep learning using rectified linear units relu"
    - "efficient database generation for data driven security assessment of power systems"
    - "adam a method for stochastic optimization"
    - "nesta the nicta energy system test case archive"

Connected through hyperedge HE-2 (induced by source paper "inverse stability problem and applications to renewables integration"):
  with members:
    - "a framework for robust assessment of power grid stability and resiliency"
    - "toward simulation free estimation of critical clearing time"

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)