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: "distributed equilibrium learning for power network voltage control with a locally connected communication network"
  Abstract: In current power distribution systems, one of the most challenging operation tasks is to coordinate the network- wide distributed energy resources (DERs) to maintain the stability of voltage magnitude of the system. This voltage control task has been investigated actively under either distributed optimization-based or local feedback control-based characterizations. The former architecture requires a strongly-connected communication network among all DERs for implementing the optimization algorithms, a scenario not yet realistic in most of the existing distribution systems with under-deployed communication infrastructure. The latter one, on the other hand, has been proven to suffer from loss of network-wide op- erational optimality. In this paper, we propose a game-theoretic characterization for semi-local voltage control with only a locally connected communication network. We analyze the existence and uniqueness of the generalized Nash equilibrium (GNE) for this characterization and develop a fully distributed equilibrium-learning algorithm that relies on only neighbor-to-neighbor information exchange. Provable convergence results are provided along with numerical tests which corroborate the robust convergence property of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"):
  with members:
    - "a multi agent off policy actor critic algorithm for distributed reinforcement learning"
    - "a theoretical analysis of deep q learning"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "information theoretic considerations in batch reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "dynamic power distribution system management with a locally connected communication network"
    - "communication efficient distributed reinforcement learning"
    - "distributed learning of average belief over networks using sequential observations"

Connected through hyperedge HE-2 (induced by source paper "distributed learning of average belief over networks using sequential observations"):
  with members:
    - "dynamic power distribution system management with a locally connected communication network"
    - "learning and management for internet of things accounting for adaptivity and scalability"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-3 (induced by source paper "dynamic power distribution system management with a locally connected communication network"):
  with members:
    - "decentralized dynamic optimization for power network voltage control"
    - "efficient computation of sensitivity coefficients of node voltages and line currents in unbalanced radial electrical distribution networks"

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)