human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stochastic games for smart grid energy management with prospect prosumers"):
  Abstract: In this paper, the problem of smart grid energy management under stochastic dynamics is investigated. In the considered model, at the demand side, it is assumed that customers can act as prosumers who own renewable energy sources and can both produce and consume energy. Due to the coupling between the prosumers' decisions and the stochastic nature of renewable energy, the interaction among prosumers is formulated as a stochastic game, in which each prosumer seeks to maximize its payoff, in terms of revenues, by controlling its energy consumption and demand. In particular, the subjective behavior of prosumers is explicitly reflected into their payoff functions using prospect theory, a powerful framework that allows modeling real-life human choices. For this prospect-based stochastic game, it is shown that there always exists a stationary Nash equilibrium where the prosumers' trading policies in the equilibrium are independent of the time and their histories of the play. Moreover, a novel distributed algorithm with no information sharing among prosumers is proposed and shown to converge to an $\epsilon$-Nash equilibrium. On the other hand, at the supply side, the interaction between the utility company and the prosumers is formulated as an online optimization problem in which the utility company's goal is to learn its optimal energy allocation rules. For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori. Simulation results show the convergence of the proposed algorithms to their predicted outcomes and present new insights resulting from prospect theory that contribute toward more efficient energy management in the smart grids.

Includes members:
  Member "load shifting in the smart grid to participate or not":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "managing price uncertainty in prosumer centric energy trading a prospect theoretic stackelberg game approach")
  Member "towards a consumer centric grid a behavioral perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prospect theory for human centric communications")
  Member "cumulative prospect theory meets reinforcement learning prediction and control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "planning under risk and uncertainty based on prospect theoretic models")
  Member "integrating energy storage into the smart grid a prospect theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")
  Member "cooperation and storage tradeoffs in power grids with renewable energy resources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "peer to peer energy trading with sustainable user participation a game theoretic approach")
  Member "a game theoretic approach to energy trading in the smart grid":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when energy trading meets blockchain in electrical power system the state of the art")
  Member "optimal power cost management using stored energy in data centers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online inventory management with application to energy procurement in data centers")
  Member "learning equilibria with partial information in decentralized wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interference coordination via power domain channel estimation")

Question: Please tell me which class the source paper 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.GT(Computer Science and Game Theory)