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: "feeder topology identification"
  Abstract: Advanced control of distributed energy resources at the consumer level requires full situational awareness of the the distribution system. One important problem is that of feeder topology identification, due to changing switch configurations, given a sparse number of measurements. We formulate the problem for residential feeders as a spanning tree identification problem over a general graph. Given a set of power flow measurements and load pseudo measurements, we show that the underlying graph structure is crucial in defining identifiability of the correct spanning tree on the graph. First we solve the deterministic case of known true loads and measured power flow. We show that the placement of sensors on the network alone determine whether the set of spanning trees can be correctly identified. In the stochastic case where loads are in the form of noisy forecasts, we present a locally optimal sensor placement algorithm.

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"
    - "data driven approach for distribution network topology detection"

Connected through hyperedge HE-2 (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"
    - "data driven approach for distribution network topology detection"
    - "grid topology identification using electricity prices"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-3 (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"
    - "data driven approach for distribution network topology detection"

Connected through hyperedge HE-4 (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"
    - "data driven approach for distribution network topology detection"

Connected through hyperedge HE-5 (induced by source paper "exact topology reconstruction of radial dynamical systems with applications to distribution system of the power grid"):
  with members:
    - "identifying independencies in causal graphs with feedback"

Connected through hyperedge HE-6 (induced by source paper "exact topology and parameter estimation in distribution grids with minimal observability"):
  with members:
    - "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)