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: "ensemble control of cycling energy loads markov decision approach"
  Abstract: A Markov decision process (MDP) framework is adopted to represent ensemble control of devices with cyclic energy consumption patterns, e.g., thermostatically controlled loads. Specifically we utilize and develop the class of MDP models previously coined linearly solvable MDPs, that describe optimal dynamics of the probability distribution of an ensemble of many cycling devices. Two principally different settings are discussed. First, we consider optimal strategy of the ensemble aggregator balancing between minimization of the cost of operations and minimization of the ensemble welfare penalty, where the latter is represented as a KL-divergence between actual and normal probability distributions of the ensemble. Then, second, we shift to the demand response setting modeling the aggregator’s task to minimize the welfare penalty under the condition that the aggregated consumption matches the targeted time-varying consumption requested by the system operator. We discuss a modification of both settings aimed at encouraging or constraining the transitions between different states. The dynamic programming feature of the resulting modified MDPs is always preserved; however, “linear solvability” is lost fully or partially, depending on the type of modification. We also conducted some (limited in scope) numerical experimentation using the formulations of the first setting. We conclude by discussing future generalizations and applications.

Connected through hyperedge HE-1 (induced by source paper "a markov process approach to ensemble control of smart buildings"):
  with members:
    - "optimal load ensemble control in chance constrained optimal power flow"
    - "optimal ensemble control of loads in distribution grids with network constraints"

Connected through hyperedge HE-2 (induced by source paper "optimal load ensemble control in chance constrained optimal power flow"):
  with members:
    - "chance constraints for improving the security of ac optimal power flow"
    - "chance constrained admm approach for decentralized control of distributed energy resources"
    - "optimal ensemble control of loads in distribution grids with network constraints"
    - "a unifying framework for linearly solvable control"

Connected through hyperedge HE-3 (induced by source paper "power of ensemble diversity and randomization for energy aggregation"):
  with members:
    - "ensemble of thermostatically controlled loads statistical physics approach"

Connected through hyperedge HE-4 (induced by source paper "optimal ensemble control of loads in distribution grids with network constraints"):
  with members:
    - "ensemble of thermostatically controlled loads statistical physics approach"
    - "a unifying framework for linearly solvable control"

Connected through hyperedge HE-5 (induced by source paper "ensemble of thermostatically controlled loads statistical physics approach"):
  with members:
    - "a unifying framework for linearly solvable 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)