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: "exploration of graph computing in power system state estimation"
  Abstract: With the increased complexity of power systems due to the integration of smart grid technologies and renewable energy resources, more frequent changes have been introduced to system status, and the traditional serial mode of state estimation algorithm cannot well meet the restrict time-constrained requirement for the future dynamic power grid, even with advanced computer hardware. To guarantee the grid reliability and minimize the impacts caused by system status fluctuations, a fast, even SCADA-rate, state estimator is urgently needed. In this paper, a graph based power system modeling is firstly explored and a graph computing based state estimation is proposed to speed up its performance. The power system is represented by a graph, which is a collection of vertices and edges, and the measurements are attributes of vertices and edges. Each vertex can independently implement local computation, like formulations of the node-based H matrix, gain matrix and righthand-side (RHS) vector, only with the information on its connected edges and neighboring vertices. Then, by taking advantages of graph database, these node-based data are conveniently collected and stored in the compressed sparse row (CSR) format avoiding the complexity and heaviness introduced by the sparse matrices. With communications and synchronization, centralized computation of solving the weighted least square (WLS) state estimation is completed with hierarchical parallel computing. The proposed strategy is implemented on a graph database platform. The testing results of IEEE 14-bus, IEEE 118-bus systems and a provincial system in China verify the accuracy and high-performance of the proposed methodology.

Connected through hyperedge HE-1 (induced by source paper "graph computing based distributed state estimation with pmus"):
  with members:
    - "optimization of battery energy storage to improve power system oscillation damping"
    - "exploration of bi level pagerank algorithm for power flow analysis using graph database"
    - "cim e oriented graph database model architecture and parallel network topology processing"

Connected through hyperedge HE-2 (induced by source paper "graph computing based distributed fast decoupled power flow analysis"):
  with members:
    - "graph based preconditioning conjugate gradient algorithm for n 1 contingency analysis"
    - "exploration of bi level pagerank algorithm for power flow analysis using graph database"
    - "cim e oriented graph database model architecture and parallel network topology processing"

Connected through hyperedge HE-3 (induced by source paper "graph computing based fast screening in contingency analysis"):
  with members:
    - "graph based preconditioning conjugate gradient algorithm for n 1 contingency analysis"
    - "exploration of bi level pagerank algorithm for power flow analysis using graph database"
    - "cim e oriented graph database model architecture and parallel network topology processing"

Connected through hyperedge HE-4 (induced by source paper "a high performance energy management system based on evolving graph"):
  with members:
    - "graph based preconditioning conjugate gradient algorithm for n 1 contingency analysis"
    - "cim e oriented graph database model architecture and parallel network topology processing"

Connected through hyperedge HE-5 (induced by source paper "substation one line diagram automatic generation and visualization"):
  with members:
    - "graph based preconditioning conjugate gradient algorithm for n 1 contingency analysis"
    - "cim e oriented graph database model architecture and parallel network topology processing"

Connected through hyperedge HE-6 (induced by source paper "exploration of bi level pagerank algorithm for power flow analysis using graph database"):
  with members:
    - "cim e oriented graph database model architecture and parallel network topology processing"

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)