human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "inverse power flow problem"):
  Abstract: This study considers the inverse power flow problem which is to infer the nodal admittance matrix (and therefore the operational network structure of the power system) from voltage and current phasors measured at a number of buses. We show that the admittance matrix can be uniquely identified from a sequence of measurements of different steady states when the every node in system is measured and a reduced admittance matrix (from Kron reduction) can be determined when it contains some hidden nodes. Furthermore, we propose efficient algorithms based on graph theory and convex relaxation to uncover the actual admittance matrix of radial and mesh systems with hidden nodes. In particular, we provide theoretical guarantees of the recovered admittance matrices for radial systems. Simulations performed on standard test systems confirm that these algorithms provide accurate estimates of the admittance matrix from noisy data.

Includes members:
  Member "on identification of distribution grids":
    also connected through other hyperedges:
      (none)
  Member "on identification of distribution grids":
    also connected through other hyperedges:
      (none)
  Member "patopa a data driven parameter and topology joint estimation framework in distribution grids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "physics guided deep neural networks for powerflow analysis")
  Member "grid topology identification using electricity prices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topology estimation using graphical models in multi phase power distribution grids")

Question: Please tell me which class the source paper 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)