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 "a theory of pricing private data"):
  Abstract: Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties.   As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means.   In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.

Includes members:
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "a market for unbiased private data paying individuals according to their privacy attitudes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "locally private hypothesis selection")
  Member "conducting truthful surveys cheaply":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards data auctions with externalities")
  Member "low rank mechanism for optimizing batch queries under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convex optimization for linear query processing under approximate differential privacy")
  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.CR(Cryptography and Security)