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 "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"):
  Abstract: We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. #R##N#In this paper, the value of $\epsilon$ units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of $\epsilon$. The higher $\epsilon$ is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy $\epsilon$ units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum.

Includes members:
  Member "putting peer prediction under the micro economic scope and making truth telling focal":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control")
  Member "privacy games optimal user centric data obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ensuring privacy in location based services a model based approach")
  Member "building a rappor with the unknown privacy preserving learning of associations and data dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wireless federated learning with local differential privacy")
  Member "local private efficient protocols for succinct histograms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "redrawing the boundaries on purchasing data from privacy sensitive individuals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "buying private data without verification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal feature discovery through strategic modification")
  Member "on the relation between identifiability differential privacy and mutual information privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")

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)