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: "limiting gaming opportunities on incentive based demand response programs"
  Abstract: Abstract   Incentive-based demand response is a program where participant users are paid to reduce energy consumption from an established baseline. Counter-factual models to estimate the baselines are vulnerable for gaming. In this paper, a novel demand response contract between a user and an aggregator is developed to induce individual rationality (voluntary participation) and asymptotic incentive-compatibility (truthfulness) through the probability of call, which is the chance of a consumer to be selected by the aggregator to serve as demand response resource at a given period. In this approach, a consumer self-reports his baseline and reduction capacity, given a payment scheme that includes cost of electricity, incentive price, and penalty caused by any deviation between self-reported and actual energy consumption. Another important feature of this approach, different from the classic solutions, is that a participant agent does not require reporting marginal utility (energy preference), and only announces information in terms of energy. A two-stage stochastic programming problem is proposed from the demand side to understand the consumer rational decisions under this contract. As result, the aggregator decides randomly what users are called to perform the energy reduction in order to manage the truth-telling behavior of each agent through the probability of call. Mathematical proofs and numerical studies are provided to demonstrate the properties and advantages of this contract in limiting gaming opportunities and in terms of its implementation.

Connected through hyperedge HE-1 (induced by source paper "design benchmarking and explainability analysis of a game theoretic framework towards energy efficiency in smart infrastructure"):
  with members:
    - "a deep learning and gamification approach to energy conservation at nanyang technological university"
    - "network inference via the time varying graphical lasso"
    - "a robust utility learning framework via inverse optimization"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "a minimal incentive based demand response program with self reported baseline mechanism"):
  with members:
    - "rational consumer decisions in a peak time rebate program"
    - "mechanism design for demand response programs"

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)