human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "impact of social learning on privacy preserving data collection"):
  Abstract: We study a model where a data collector obtains data from users through a payment mechanism, aiming to learn the underlying state from the elicited data. The private signal of each user represents her knowledge about the state; and through social interactions each user can also learn noisy versions of her social friends' signals, which is called `learned group signals'. Thanks to social learning, users have richer information about the state beyond their private signals. Based on both her private signal and learned group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, in general, the desired data reporting strategy at the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. Specifically, a generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. Further, when a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. We emphasize that the reported data when a user plays the ND strategy is still informative about the underlying state because it is based on her learned group signals. As a result, both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We further derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.

Includes members:
  Member "peer prediction with heterogeneous tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "token curated registry with citation graph")
  Member "the value of privacy strategic data subjects incentive mechanisms and fundamental limits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")
  Member "learning to incentivize eliciting effort via output agreement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "informed truthfulness in multi task peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "surrogate scoring rules")
  Member "redrawing the boundaries on purchasing data from privacy sensitive individuals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "approximately optimal auctions for selling privacy when costs are correlated with data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")
  Member "truthful mechanisms for agents that value privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differential privacy at risk bridging randomness and privacy budget")
  Member "selling privacy at auction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "guidelines for implementing and auditing differentially private systems")

Question: Please tell me which class the source paper 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)