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 "truthful linear regression"):
  Abstract: We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model individuals' privacy losses. This immediately poses a problem, as differentially private computation of a linear model necessarily produces a biased estimation, and existing approaches to design mechanisms to elicit data from privacy-sensitive individuals do not generalize well to biased estimators. We overcome this challenge through an appropriate design of the computation and payment scheme.

Includes members:
  Member "private matchings and allocations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact")
  Member "budget feasible mechanisms for experimental design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causal feature discovery through strategic modification")
  Member "buying private data without verification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal feature discovery through strategic modification")
  Member "linear regression as a non cooperative game":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal feature discovery through strategic modification")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "mechanism design in large games incentives and privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentially private call auctions and market impact")
  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)