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 "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"):
  Abstract: Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies on co-design of incentive mechanism and privacy preservation assume a trustworthy fusion center (FC). Very recent work has taken steps to relax the assumption on trustworthy FC and allows participatory users (PUs) to add well calibrated noise to their raw sensing data before reporting them, whereas the focus is on the equilibrium behavior of data subjects with binary data. Making a paradigm shift, this paper aim to quantify the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer adding larger noise for higher privacy-preserving levels (PPLs). To achieve a good balance therein, we design an efficient incentive mechanism to REconcile FC's Aggregation accuracy and individual PU's data Privacy (REAP). Specifically, we adopt the celebrated notion of differential privacy to measure PUs' PPLs and quantify their impacts on FC's aggregation accuracy. Then, appealing to Contract Theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control PUs. It can further overcome the information asymmetry, i.e., the FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.

Includes members:
  Member "the value of privacy strategic data subjects incentive mechanisms and fundamental limits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")
  Member "crowdsourcing with tullock contests a new perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "small profits and quick returns an incentive mechanism design for iot based crowdsourcing under continuous platform competition")
  Member "distributed time sensitive task selection in mobile crowdsensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trajectory design for multiple uav assisted wireless networks")
  Member "redrawing the boundaries on purchasing data from privacy sensitive individuals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "privacy and mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "segmentation incentives and privacy")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally private hypothesis selection")
  Member "take it or leave it running a survey when privacy comes at a cost":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning for the manipulation of eye tracking data")
  Member "approximately optimal auctions for selling privacy when costs are correlated with data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")

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)