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: "approximately optimal auctions for selling privacy when costs are correlated with data"
  Abstract: We consider a scenario in which a database stores sensitive data of users and an analyst wants to estimate statistics of the data. The users may suffer a cost when their data are used in which case they should be compensated. The analyst wishes to get an accurate estimate, while the users want to maximize their utility. We want to design a mechanism that can estimate statistics accurately without compromising users' privacy. #R##N#Since users' costs and sensitive data may be correlated, it is important to protect the privacy of both data and cost. We model this correlation by assuming that a user's unknown sensitive data determines a distribution from a set of publicly known distributions and a user's cost is drawn from that distribution. We propose a stronger model of privacy preserving mechanism where users are compensated whenever they reveal information about their data to the mechanism. In this model, we design a Bayesian incentive compatible and privacy preserving mechanism that guarantees accuracy and protects the privacy of both cost and data.

Connected through hyperedge HE-1 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism"):
  with members:
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "managing price uncertainty in prosumer centric energy trading a prospect theoretic stackelberg game approach"
    - "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "hardware trojan detection game a prospect theoretic approach"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"

Connected through hyperedge HE-2 (induced by source paper "prior free data acquisition for accurate statistical estimation"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"
    - "optimal data acquisition for statistical estimation"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "low cost learning via active data procurement"
    - "truthful linear regression"

Connected through hyperedge HE-3 (induced by source paper "impact of social learning on privacy preserving data collection"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "truthful mechanisms for agents that value privacy"
    - "selling privacy at auction"

Connected through hyperedge HE-4 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"

Connected through hyperedge HE-5 (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"
    - "the possibilities and limitations of private prediction markets"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"):
  with members:
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "crowdsourcing with tullock contests a new perspective"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "selling privacy at auction"

Connected through hyperedge HE-7 (induced by source paper "optimized direct sale of privacy in personal data marketplaces"):
  with members:
    - "myadchoices bringing transparency and control to online advertising"
    - "you never surf alone ubiquitous tracking of users browsing habits"
    - "a theory of pricing private data"
    - "a market for unbiased private data paying individuals according to their privacy attitudes"
    - "conducting truthful surveys cheaply"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "privacy auctions for recommender systems"
    - "selling privacy at auction"

Connected through hyperedge HE-8 (induced by source paper "optimal data acquisition for statistical estimation"):
  with members:
    - "strategic classification from revealed preferences"
    - "low cost learning via active data procurement"
    - "truthful linear regression"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "buying private data without verification"
    - "conducting truthful surveys cheaply"
    - "selling privacy at auction"

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)