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: "efficient computation of sensitivity coefficients of node voltages and line currents in unbalanced radial electrical distribution networks"
  Abstract: The problem of optimal control of power distribution systems is becoming increasingly compelling due to the progressive penetration of distributed energy resources in this specific layer of the electrical infrastructure. Distribution systems are, indeed, experiencing significant changes in terms of operation philosophies that are often based on optimal control strategies relying on the computation of linearized dependencies between controlled (e.g., voltages, frequency in case of islanding operation) and control variables (e.g., power injections, transformers tap positions). As the implementation of these strategies in real-time controllers imposes stringent time constraints, the derivation of analytical dependency between controlled and control variables becomes a non-trivial task to be solved. With reference to optimal voltage and power flow controls, this paper aims at providing an analytical derivation of node voltages and line currents as a function of the nodal power injections and transformers tap-changers positions. Compared to other approaches presented in the literature, the one proposed here is based on the use of the [Y] compound matrix of a generic multi-phase radial unbalanced network. In order to estimate the computational benefits of the proposed approach, the relevant improvements are also quantified versus traditional methods. The validation of the proposed method is carried out by using both IEEE 13 and 34 nodes test feeders. The paper finally shows the use of the proposed method for the problem of optimal voltage control applied to the IEEE 34 node test feeder.

Connected through hyperedge HE-1 (induced by source paper "multivariate boosted trees and applications to forecasting and control"):
  with members:
    - "hierarchical demand forecasting benchmark for the distribution grid"
    - "geometric deep learning going beyond euclidean data"
    - "xgboost a scalable tree boosting system"
    - "deep neural networks regularization for structured output prediction"

Connected through hyperedge HE-2 (induced by source paper "optimal voltage regulation of unbalanced distribution networks with coordination of oltc and pv generation"):
  with members:
    - "optimal oltc voltage control scheme to enable high solar penetrations"
    - "smart inverter impacts on california distribution feeders with increasing pv penetration a case study"

Connected through hyperedge HE-3 (induced by source paper "real time enforcement of local energy market transactions respecting distribution grid constraints"):
  with members:
    - "novel market approach for locally balancing renewable energy production and flexible demand"

Connected through hyperedge HE-4 (induced by source paper "decentralized p2p energy trading under network constraints in a low voltage network"):
  with members:
    - "exogenous approach to grid cost allocation in peer to peer electricity markets"
    - "a study of energy trading in a low voltage network centralised and distributed approaches"
    - "a component based dual decomposition method for the opf problem"
    - "a fast distributed algorithm for large scale demand response aggregation"
    - "a game theoretic approach to energy trading in the smart grid"

Connected through hyperedge HE-5 (induced by source paper "dynamic power distribution system management with a locally connected communication network"):
  with members:
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"
    - "decentralized dynamic optimization for power network voltage control"

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)