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: "ergodic energy management leveraging resource variability in distribution grids"
  Abstract: Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.

Connected through hyperedge HE-1 (induced by source paper "cross layer scheduling and beamforming in smart grid powered cellular networks with heterogeneous energy coordination"):
  with members:
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "robust energy efficient beamforming in misome swipt systems with proportional secrecy rate"
    - "dynamic cross layer beamforming in hybrid powered communication systems with harvest use trade strategy"
    - "a hybrid energy sharing framework for green cellular networks"
    - "an overview of sustainable green 5g networks"
    - "comp meets smart grid a new communication and energy cooperation paradigm"
    - "power delay tradeoff with predictive scheduling in integrated cellular and wi fi networks"

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"
    - "voltage regulation algorithms for multiphase power 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"
    - "voltage regulation algorithms for multiphase power distribution grids"

Connected through hyperedge HE-4 (induced by source paper "power system state estimation via feasible point pursuit algorithms and cramer rao bound"):
  with members:
    - "moving horizon dynamic power system state estimation using semidefinite relaxation"

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)