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 propagation model for provenance views of public private workflows"):
  Abstract: We study the problem of concealing functionality of a proprietary or private module when provenance information is shown over repeated executions of a workflow which contains both `public' and `private' modules. Our approach is to use `provenance views' to hide carefully chosen subsets of data over all executions of the workflow to ensure G-privacy: for each private module and each input x, the module's output f(x) is indistinguishable from G -1 other possible values given the visible data in the workflow executions. We show that G-privacy cannot be achieved simply by combining solutions for individual private modules; data hiding must also be `propagated' through public modules. We then examine how much additional data must be hidden and when it is safe to stop propagating data hiding. The answer depends strongly on the workflow topology as well as the behavior of public modules on the visible data. In particular, for a class of workflows (which include the common tree and chain workflows), taking private solutions for each private module, augmented with a `public closure' that is `upstream-downstream safe', ensures G-privacy. We define these notions formally and show that the restrictions are necessary. We also study the related optimization problems of minimizing the amount of hidden data.

Includes members:
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "differentially private publication of sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack")
  Member "surrogate parenthood protected and informative graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an analytical survey of provenance sanitization")
  Member "transparent anonymization thwarting adversaries who know the algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "publishing microdata with a robust privacy guarantee")
  Member "provenance views for module privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "viewpoint personal data and the internet of things it is time to care about digital provenance")
  Member "differential privacy via wavelet transforms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments")
  Member "composition attacks and auxiliary information in data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local differential privacy for deep learning")

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)