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: "risk limiting load restoration for resilience enhancement with intermittent energy resources"
  Abstract: Microgrids are resources to restore critical loads after a natural disaster, enhancing the resiliency of a distribution network. To deal with stochastic power generated by intermittent energy resources within microgrids, such as wind turbines (WTs) and photovoltaics (PVs), most existing methods require forecast information. However, some microgrids may not be equipped with power forecasting tools. To fill this gap, a risk-limiting strategy based on real-time measurements is proposed. The Gaussian mixture model (GMM) is used to represent a prior joint distribution of power outputs of WTs and PVs over multiple periods based on historical data. As time rolls forward, with latest measurement data, the distributions of WT/PV generation are updated in a recursive manner. The updated distributions are used as inputs for load restoration, enabling an equivalent transformation of the original probabilistic-constrained problem to a mixed integer linear programming (MILP) that is computationally tractable. Simulation cases on a distribution system with three microgrids demonstrate the effectiveness of the proposed method. Results also indicate that networked microgrids have better uncertainty management capabilities than stand-alone microgrids.

Connected through hyperedge HE-1 (induced by source paper "a distributed incremental update scheme for probability distribution of wind power forecast error"):
  with members:
    - "privacy preserving distributed joint probability modeling for spatial correlated wind farms"
    - "scalable and incremental learning of gaussian mixture models"
    - "a fast incremental gaussian mixture model"

Connected through hyperedge HE-2 (induced by source paper "a distributed privacy preserving incremental update algorithm for probability distribution of wind power forecast error"):
  with members:
    - "scalable and incremental learning of gaussian mixture models"
    - "a fast incremental gaussian mixture model"

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)