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: "a game theoretic study on non monetary incentives in data analytics projects with privacy implications"
  Abstract: The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoff scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibria that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.

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 "fair multi party machine learning a game theoretic approach"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "deep residual learning for image recognition"
    - "linear regression as a non cooperative game"
    - "linear regression from strategic data sources"

Connected through hyperedge HE-3 (induced by source paper "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"):
  with members:
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "quantitative models of imperfect deception in network security using signaling games with evidence"
    - "information leakage games"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "deception by design evidence based signaling games for network defense"
    - "privacy games optimal user centric data obfuscation"
    - "flip the cloud cyber physical signaling games in the presence of advanced persistent threats"

Connected through hyperedge HE-4 (induced by source paper "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"):
  with members:
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "privacy constrained information processing"
    - "selling privacy at auction"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "a stackelberg game perspective on the conflict between machine learning and data obfuscation"):
  with members:
    - "selling privacy at auction"
    - "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)