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: "assessing the privacy cost in centralized event based demand response for microgrids"
  Abstract: Demand response (DR) programs have emerged as a potential key enabling ingredient in the context of smart grid (SG). Nevertheless, the rising concerns over privacy issues raised by customers subscribed to these programs constitute a formidable hurdle towards their effective deployment and utilization. This has driven extensive research to resolve the hindrance confronted, resulting in a number of methods being proposed for preserving customers' privacy. While these methods provide stringent privacy guarantees, only limited attention has been paid to their computational efficiency and performance quality. Under the paradigm of differential privacy, this paper initiates a systematic empirical study on quantifying the trade-off between privacy and optimality in centralized DR systems for maximizing cumulative customer utility. Aiming to elucidate the factors governing this trade-off, the privacy cost is evaluated in terms of changes in objective value of the DR optimization problem when effecting the employed privacy-preserving strategy based on Laplace mechanism. The analytical results presented herein are complemented with empirical findings, corroborated extensively by simulations with up to thousands of customers on a realistic 4-bus microgrid (MG) model embracing the underlying power distribution network properties and AC power flow constraints. By evaluating the privacy impact, this pilot study serves DR practitioners when considering the social and economic implications of deploying privacy-preserving DR programs in practice. Moreover, it stimulates further research to explore more efficient privacy solutions for energy procurement of MGs with bounded constant optimality guarantees.

Connected through hyperedge HE-1 (induced by source paper "differential privacy for power grid obfuscation"):
  with members:
    - "differential privacy of aggregated dc optimal power flow data"
    - "privacy preserving obfuscation of critical infrastructure networks"
    - "the influence of differential privacy on short term electric load forecasting"
    - "architecture and algorithms for privacy preserving thermal inertial load management by a load serving entity"
    - "optimality of the laplace mechanism in differential privacy"
    - "nesta the nicta energy system test case archive"

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)