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: "energy coupon a mean field game perspective on demand response in smart grids"
  Abstract: We consider the problem of a Load Serving Entity (LSE) trying to reduce its exposure to electricity market volatility by incentivizing demand response in a Smart Grid setting. We focus on the day-ahead electricity market, wherein the LSE has a good estimate of the statistics of the wholesale price of electricity at different hours in the next day, and wishes its customers to move a part of their power consumption to times of low mean and variance in price. Based on the time of usage, the LSE awards a differential number of "Energy Coupons" to each customer in proportion to the customer's electricity usage at that time. A lottery is held periodically in which the coupons held by all the customers are used as lottery tickets. #R##N#Our study takes the form of a Mean Field Game, wherein each customer models the number of coupons that each of its opponents possesses via a distribution, and plays a best response pattern of electricity usage by trading off the utility of winning at the lottery versus the discomfort suffered by changing its usage pattern. The system is at a Mean Field Equilibrium (MFE) if the number of coupons that the customer receives is itself a sample drawn from the assumed distribution. We show the existence of an MFE, and characterize the mean field customer policy as having a multiple-threshold structure in which customers who have won too frequently or infrequently have low incentives to participate. We then numerically study the system with a candidate application of air conditioning during the summer months in the state of Texas. Besides verifying our analytical results, we show that the LSE can potentially attain quite substantial savings using our scheme. Our techniques can also be applied to resource sharing problems in other \emph{societal} networks such as transportation or communication.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for mean field game"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "equilibria of dynamic games with many players existence approximation and market structure"

Connected through hyperedge HE-2 (induced by source paper "reputation based information design for inducing prosocial behavior"):
  with members:
    - "informational braess paradox the effect of information on traffic congestion"
    - "mean field games in nudge systems for societal networks"

Connected through hyperedge HE-3 (induced by source paper "optimal mechanism design with risk loving agents"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "revenue maximization with an uncertainty averse buyer"
    - "selling two goods optimally"
    - "duality and optimality of auctions for uniform distributions"
    - "strong duality for a multiple good monopolist"
    - "mechanism design via optimal transport"
    - "prior independent auctions for risk averse agents"
    - "mechanisms for risk averse agents without loss"

Connected through hyperedge HE-4 (induced by source paper "incentivizing sharing in realtime d2d streaming networks a mean field game perspective"):
  with members:
    - "a mean field game approach to scheduling in cellular systems"
    - "a non monetary protocol for peer to peer content distribution in wireless broadcast networks with network coding"

Connected through hyperedge HE-5 (induced by source paper "mean field games in nudge systems for societal networks"):
  with members:
    - "incentivizing sharing in realtime d2d streaming networks a mean field game perspective"
    - "integrating energy storage into the smart grid a prospect theoretic approach"
    - "incentive mechanisms for internet congestion management fixed budget rebate versus time of day pricing"
    - "a mean field game approach to scheduling in cellular systems"
    - "equilibria of dynamic games with many players existence approximation and market structure"

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.GT(Computer Science and Game Theory)