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 "take it or leave it running a survey when privacy comes at a cost"):
  Abstract: In this paper, we consider the problem of estimating a potentially sensitive (individually stigmatizing) statistic on a population. In our model, individuals are concerned about their privacy, and experience some cost as a function of their privacy loss. Nevertheless, they would be willing to participate in the survey if they were compensated for their privacy cost. These cost functions are not publicly known, however, nor do we make Bayesian assumptions about their form or distribution. Individuals are rational and will misreport their costs for privacy if doing so is in their best interest. Ghosh and Roth recently showed in this setting, when costs for privacy loss may be correlated with private types, if individuals value differential privacy, no individually rational direct revelation mechanism can compute any non-trivial estimate of the population statistic. In this paper, we circumvent this impossibility result by proposing a modified notion of how individuals experience cost as a function of their privacy loss, and by giving a mechanism which does not operate by direct revelation. Instead, our mechanism has the ability to randomly approach individuals from a population and offer them a take-it-or-leave-it offer. This is intended to model the abilities of a surveyor who may stand on a street corner and approach passers-by.

Includes members:
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "conducting truthful surveys cheaply":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards data auctions with externalities")
  Member "the exponential mechanism for social welfare private truthful and nearly optimal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a differentially private incentive design for traffic offload to public transportation")
  Member "approximately optimal auctions for selling privacy when costs are correlated with data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "differential privacy at risk bridging randomness and privacy budget")
  Member "privacy aware mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "price of privacy in the keynesian beauty contest")
  Member "selling privacy at auction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "approximately optimal mechanism design via differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "facility location problem in differential privacy model revisited")

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)