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: "privacy aware mechanism design"
  Abstract: In traditional mechanism design, agents only care about the utility they derive from the outcome of the mechanism. We look at a richer model where agents also assign non-negative dis-utility to the information about their private types leaked by the outcome of the mechanism. #R##N#We present a new model for privacy-aware mechanism design, where we only assume an upper bound on the agents' loss due to leakage, as opposed to previous work where a full characterization of the loss was required. #R##N#In this model, under a mild assumption on the distribution of how agents value their privacy, we show a generic construction of privacy-aware mechanisms and demonstrate its applicability to electronic polling and pricing of a digital good.

Connected through hyperedge HE-1 (induced by source paper "price of privacy in the keynesian beauty contest"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "linear queries estimation with local differential privacy"
    - "locally private gaussian estimation"
    - "perception games and privacy"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "truthful mechanisms for agents that value privacy"
    - "what can we learn privately"

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"
    - "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-3 (induced by source paper "adversarial contract design for private data commercialization"):
  with members:
    - "blockchain enabled data marketplace design and challenges"
    - "implementation of smart contracts using hybrid architectures with on and off blockchain components"
    - "contract design for energy demand response"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "buying private data without verification"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "differential privacy an economic method for choosing epsilon"
    - "a theory of pricing private data"

Connected through hyperedge HE-4 (induced by source paper "segmentation incentives and privacy"):
  with members:
    - "the strange case of privacy in equilibrium models"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-5 (induced by source paper "sharing information with competitors"):
  with members:
    - "how to incentivize data driven collaboration among competing parties"
    - "a new approach to practical active secure two party computation"
    - "rational secret sharing and multiparty computation extended abstract"

Connected through hyperedge HE-6 (induced by source paper "perception games and privacy"):
  with members:
    - "the empirical implications of privacy aware choice"
    - "truthful mechanisms for agents that value privacy"

Connected through hyperedge HE-7 (induced by source paper "truthful linear regression"):
  with members:
    - "private matchings and allocations"
    - "budget feasible mechanisms for experimental design"
    - "buying private data without verification"
    - "linear regression as a non cooperative game"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"

Connected through hyperedge HE-8 (induced by source paper "the strange case of privacy in equilibrium models"):
  with members:
    - "privacy preserving public information for sequential games"
    - "calibrating data to sensitivity in private data analysis"
    - "fairness through awareness"

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)