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 "differential privacy an economic method for choosing epsilon"):
  Abstract: Differential privacy is becoming a gold standard for privacy research; it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of interesting problems. #R##N#However, the question of when differential privacy works in practice has received relatively little attention. In particular, there is still no rigorous method for choosing the key parameter $\epsilon$, which controls the crucial tradeoff between the strength of the privacy guarantee and the accuracy of the published results. #R##N#In this paper, we examine the role that these parameters play in concrete applications, identifying the key questions that must be addressed when choosing specific values. This choice requires balancing the interests of two different parties: the data analyst and the prospective participant, who must decide whether to allow their data to be included in the analysis. We propose a simple model that expresses this balance as formulas over a handful of parameters, and we use our model to choose $\epsilon$ on a series of simple statistical studies. We also explore a surprising insight: in some circumstances, a differentially private study can be more accurate than a non-private study for the same cost, under our model. Finally, we discuss the simplifying assumptions in our model and outline a research agenda for possible refinements.

Includes members:
  Member "a theory of pricing private data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal pricing of internet of things a machine learning approach")
  Member "a market for unbiased private data paying individuals according to their privacy attitudes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimized direct sale of privacy in personal data marketplaces")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "conducting truthful surveys cheaply":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards data auctions with externalities")
  Member "privbasis frequent itemset mining with differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluating differentially private machine learning in practice")
  Member "an adaptive mechanism for accurate query answering under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "privacy preserving record linkage via grams projections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the dirichlet mechanism for differential privacy on the unit simplex")
  Member "take it or leave it running a survey when privacy comes at a cost":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning for the manipulation of eye tracking data")

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.DB(Databases)