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: "the possibilities and limitations of private prediction markets"
  Abstract: We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants' actions or beliefs. Our goal is to design market mechanisms in which participants' trades or wagers influence the market's behavior in a way that leads to accurate predictions, yet no single participant has too much influence over what others are able to observe. We study the possibilities and limitations of such mechanisms using tools from differential privacy. We begin by designing a private one-shot wagering mechanism in which bettors specify a belief about the likelihood of a future event and a corresponding monetary wager. Wagers are redistributed among bettors in a way that more highly rewards those with accurate predictions. We provide a class of wagering mechanisms that are guaranteed to satisfy truthfulness, budget balance in expectation, and other desirable properties while additionally guaranteeing epsilon-joint differential privacy in the bettors' reported beliefs, and analyze the trade-off between the achievable level of privacy and the sensitivity of a bettor's payment to her own report. We then ask whether it is possible to obtain privacy in dynamic prediction markets, focusing our attention on the popular cost-function framework in which securities with payments linked to future events are bought and sold by an automated market maker. We show that under general conditions, it is impossible for such a market maker to simultaneously achieve bounded worst-case loss and epsilon-differential privacy without allowing the privacy guarantee to degrade extremely quickly as the number of trades grows, making such markets impractical in settings in which privacy is valued. We conclude by suggesting several avenues for potentially circumventing this lower bound.

Connected through hyperedge HE-1 (induced by source paper "scalable and jointly differentially private packing"):
  with members:
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "differentially private contextual linear bandits"
    - "private matchings and allocations"
    - "privacy odometers and filters pay as you go composition"
    - "jointly private convex programming"
    - "truthful linear regression"
    - "local private efficient protocols for succinct histograms"
    - "coordination complexity small information coordinating large populations"

Connected through hyperedge HE-2 (induced by source paper "individual sensitivity preprocessing for data privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "tight lower bounds for locally differentially private selection"
    - "adaptive learning with robust generalization guarantees"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "heterogeneous differential privacy"
    - "truthful linear regression"
    - "buying private data without verification"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "differential privacy for growing databases"):
  with members:
    - "deep models under the gan information leakage from collaborative deep learning"
    - "adaptive learning with robust generalization guarantees"
    - "algorithmic stability for adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "differentially private empirical risk minimization"

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)