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: "voltage regulation algorithms for multiphase power distribution grids"
  Abstract: Time-varying renewable energy generation can result in serious under-/over-voltage conditions in future distribution grids. Augmenting conventional utility-owned voltage regulating equipment with the reactive power capabilities of distributed generation units is a viable solution. Local control options attaining global voltage regulation optimality at fast convergence rates is the goal here. In this context, novel reactive power control rules are analyzed under a unifying linearized grid model. For single-phase grids, our proximal gradient scheme has computational complexity comparable to that of the rule suggested by the IEEE 1547.8 standard, but it enjoys well-characterized convergence guarantees. Adding memory to the scheme results in accelerated convergence. For three-phase grids, it is shown that reactive injections have a counter-intuitive effect on bus voltage magnitudes across phases. Nevertheless, when our control scheme is applied to unbalanced conditions, it is shown to reach an equilibrium point. Yet this point may not correspond to the minimizer of a voltage regulation problem. Numerical tests using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison 47-bus feeder with increased renewable penetration verify the convergence properties of the schemes and their resiliency to grid topology reconfigurations.

Connected through hyperedge HE-1 (induced by source paper "real time power system state estimation and forecasting via deep unrolled neural networks"):
  with members:
    - "a survey on state estimation techniques and challenges in smart distribution systems"
    - "a learning to infer method for real time power grid multi line outage identification"
    - "learning to optimize training deep neural networks for interference management"
    - "power system state estimation via feasible point pursuit algorithms and cramer rao bound"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "robust and scalable power system state estimation via composite optimization"
    - "psse redux convex relaxation decentralized robust and dynamic approaches"
    - "a learning to infer method for real time power grid topology identification"

Connected through hyperedge HE-2 (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"
    - "real time power system state estimation and forecasting via deep unrolled neural networks"
    - "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"
    - "ergodic energy management leveraging resource variability in distribution grids"

Connected through hyperedge HE-3 (induced by source paper "two timescale voltage control in distribution grids using deep reinforcement learning"):
  with members:
    - "real time power system state estimation and forecasting via deep unrolled neural networks"
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"
    - "ergodic energy management leveraging resource variability in distribution grids"

Connected through hyperedge HE-4 (induced by source paper "decentralized dynamic optimization for power network voltage control"):
  with members:
    - "a class of prediction correction methods for time varying convex optimization"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "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)