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: "on the inverse power flow problem"
  Abstract: This paper studies the inverse power flow problem which is to infer line and transformer parameters, and the operational structure of a power system from time-synchronized measurements of voltage and current phasors at various locations. We show that the nodal admittance matrix can be uniquely identified from a sequence of steady-state measurements when the system is fully observable, and a reduced admittance matrix, from Kron reduction, can be determined when the system contains some hidden nodes. Furthermore, we discuss conditions for identifying the full admittance matrix of a power system with hidden nodes and propose efficient algorithms based on graph theory and convex relaxation to determine the admittance matrix of both radial and mesh systems when these conditions are satisfied. Simulations performed on a standard test system where all nodes are monitored confirm that the proposed algorithms can provide an accurate estimate of the admittance matrix from noisy synchrophasor data.

Connected through hyperedge HE-1 (induced by source paper "physics guided deep neural networks for powerflow analysis"):
  with members:
    - "real time power system state estimation and forecasting via deep unrolled neural networks"
    - "online deep learning learning deep neural networks on the fly"
    - "data driven power flow linearization a regression approach"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "mapping rule estimation for power flow analysis in distribution grids"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from linear measurements fundamental trade offs and applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "failure localization in power systems via tree partitions"
    - "learning identifiable gaussian bayesian networks in polynomial time and sample complexity"
    - "exact topology and parameter estimation in distribution grids with minimal observability"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "information theoretic limits of bayesian network structure learning"
    - "learning sparse graphs under smoothness prior"

Connected through hyperedge HE-3 (induced by source paper "distribution grid admittance estimation with limited non synchronized measurements"):
  with members:
    - "learning with end users in distribution grids topology and parameter estimation"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"

Connected through hyperedge HE-4 (induced by source paper "on identification of distribution grids"):
  with members:
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "data driven approach for distribution network topology detection"

Connected through hyperedge HE-5 (induced by source paper "a learning to infer method for real time power grid multi line outage identification"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "patopaem a data driven parameter and topology joint estimation framework for time varying system in distribution grids"):
  with members:
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"

Connected through hyperedge HE-7 (induced by source paper "unbalanced three phase distribution grid topology estimation and bus phase identification"):
  with members:
    - "topology estimation using graphical models in multi phase power distribution grids"

Connected through hyperedge HE-8 (induced by source paper "on identification of distribution grids"):
  with members:
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "data driven approach for distribution network topology detection"

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)