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: "real time power system state estimation and forecasting via deep unrolled neural networks"
  Abstract: Contemporary power grids are being challenged by rapid and sizeable voltage fluctuations that are caused by large-scale deployment of renewable generators, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large scale and nonconvexity, existing power system state estimation (PSSE) schemes become computationally expensive or often yield suboptimal performance. To bypass these hurdles, this paper advocates physics-inspired deep neural networks (DNNs) for real-time power system monitoring. By unrolling an iterative solver that was originally developed using the exact ac model, a novel model-specific DNN is developed for real-time PSSE requiring only offline training and minimal tuning effort. To further enable system awareness, even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are also pursued for power system state forecasting. Deep RNNs leverage the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Numerical tests showcase improved performance of the proposed DNN-based estimation and forecasting approaches compared with existing alternatives. In real load data experiments on the IEEE 118-bus benchmark system, the novel model-specific DNN-based PSSE scheme outperforms nearly by an order-of-magnitude its competing alternatives, including the widely adopted Gauss–Newton PSSE solver.

Connected through hyperedge HE-1 (induced by source paper "tensor graph convolutional networks for multi relational and robust learning"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "gated graph convolutional recurrent neural networks"
    - "adversarial attack on graph structured data"
    - "predicting multicellular function through multi layer tissue networks"
    - "filtering random graph processes over random time varying graphs"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "physics guided deep neural networks for powerflow analysis"):
  with members:
    - "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"
    - "on the inverse power flow problem"

Connected through hyperedge HE-3 (induced by source paper "real time voltage control using deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "autonomous voltage control for grid operation using deep reinforcement learning"
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "voltage regulation algorithms for multiphase power distribution grids"
    - "ergodic energy management leveraging resource variability in distribution grids"

Connected through hyperedge HE-4 (induced by source paper "two timescale voltage control in distribution grids using deep reinforcement learning"):
  with members:
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"
    - "voltage regulation algorithms for multiphase power distribution grids"
    - "ergodic energy management leveraging resource variability in distribution grids"

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.LG(Machine Learning)