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: "data driven approach for distribution network topology detection"
  Abstract: This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on the real time analysis of time-series voltages measurements. The analysis approach draws on data from high-precision phasor measurement units (μPMUs or synchrophasors) for distribution networks. The key fact is that time-series measurement data taken from the distribution network has specific patterns representing state transitions such as topology changes. The proposed algorithm is based on comparison of actual voltage measurements with a library of signatures derived from the possible topologies simulation. The IEEE 33-bus model is used for the algorithm validation.

Connected through hyperedge HE-1 (induced by source paper "graphical models in loopy distribution grids topology estimation change detection and limitation"):
  with members:
    - "topology estimation using graphical models in multi phase power distribution grids"
    - "exact topology reconstruction of radial dynamical systems with applications to distribution system of the power grid"
    - "exact topology and parameter estimation in distribution grids with minimal observability"
    - "feeder topology identification"

Connected through hyperedge HE-2 (induced by source paper "on identification of distribution grids"):
  with members:
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "on the inverse power flow problem"

Connected through hyperedge HE-3 (induced by source paper "topology estimation using graphical models in multi phase power distribution grids"):
  with members:
    - "exact topology reconstruction of radial dynamical systems with applications to distribution system of the power grid"
    - "online learning of power transmission dynamics"
    - "feeder topology identification"
    - "grid topology identification using electricity prices"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-4 (induced by source paper "joint estimation of topology injection statistics in distribution grids with missing nodes"):
  with members:
    - "topology estimation using graphical models in multi phase power distribution grids"
    - "exact topology reconstruction of radial dynamical systems with applications to distribution system of the power grid"
    - "feeder topology identification"

Connected through hyperedge HE-5 (induced by source paper "learning with end users in distribution grids topology and parameter estimation"):
  with members:
    - "topology estimation using graphical models in multi phase power distribution grids"
    - "feeder topology identification"

Connected through hyperedge HE-6 (induced by source paper "on identification of distribution grids"):
  with members:
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "on the inverse power flow problem"

Connected through hyperedge HE-7 (induced by source paper "exact topology and parameter estimation in distribution grids with minimal observability"):
  with members:
    - "feeder topology identification"

Connected through hyperedge HE-8 (induced by source paper "micro synchrophasors for power distribution monitoring a technology review"):
  with members:
    - "topology detection in microgrids with micro synchrophasors"
    - "phase identification in distribution networks with micro synchrophasors"

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)