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: "patopa a data driven parameter and topology joint estimation framework in distribution grids"
  Abstract: The increasing integration of distributed energy resources (DERs) calls for new planning and operational tools. However, such tools depend on system topology and line parameters, which may be missing or inaccurate in distribution grids. With abundant data, one idea is to use linear regression to find line parameters, based on which topology can be identified. Unfortunately, the linear regression method is accurate only if there is no noise in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). For topology estimation, even with a small error in measurements, the regression-based method is incapable of finding the topology using non-zero line parameters with a proper metric. To model input and output measurement errors simultaneously, we propose the error-in-variables (EIV) model in a maximum likelihood estimation (MLE) framework for joint line parameter and topology estimation. While directly solving the problem is NP-hard, we successfully adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. For accurate topology estimation, we let it interact with parameter estimation in a fashion that is similar to expectation-maximization fashion in machine learning. The proposed PaToPa approach does not require a radial network setting and works for mesh networks. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from South California Edison.

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"
    - "mapping rule estimation for power flow analysis in distribution grids"
    - "on the inverse power flow problem"

Connected through hyperedge HE-2 (induced by source paper "inverse power flow problem"):
  with members:
    - "on identification of distribution grids"
    - "on identification of distribution grids"
    - "grid topology identification using electricity prices"

Connected through hyperedge HE-3 (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"
    - "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"
    - "on the inverse power flow problem"

Connected through hyperedge HE-4 (induced by source paper "a survey on state estimation techniques and challenges in smart distribution systems"):
  with members:
    - "review of smart meter data analytics applications methodologies and challenges"
    - "robust recovery of missing data in electricity distribution systems"
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"
    - "topology detection in microgrids with micro synchrophasors"

Connected through hyperedge HE-5 (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"
    - "on the inverse power flow problem"

Connected through hyperedge HE-6 (induced by source paper "on identification of distribution grids"):
  with members:
    - "on the inverse power flow problem"
    - "data driven approach for distribution network topology detection"

Connected through hyperedge HE-7 (induced by source paper "patopaem a data driven parameter and topology joint estimation framework for time varying system in distribution grids"):
  with members:
    - "on the inverse power flow problem"

Connected through hyperedge HE-8 (induced by source paper "vader visualization and analytics for distributed energy resources"):
  with members:
    - "distribution system topology detection using consumer load and line flow measurements"
    - "topology detection in microgrids with micro synchrophasors"
    - "outage detection in power distribution networks"
    - "optimal inverter var control in distribution systems with high pv penetration"

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)