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 "constructing privacy channels from information channels"):
  Abstract: Data privacy protection studies how to query a dataset while preserving the privacy of individuals whose sensitive information is contained in the dataset. The information privacy model protects the privacy of an individual by using a noisy channel, called privacy channel, to filter out most information of the individual from the query's output. #R##N#This paper studies how to construct privacy channels, which is challenging since it needs to evaluate the maximal amount of disclosed information of each individual contained in the query's output, called individual channel capacity. Our main contribution is an interesting result which can transform the problem of evaluating a privacy channel's individual channel capacity, which equals the problem of evaluating the capacities of an infinite number of channels, into the problem of evaluating the capacities of a finite number of channels. This result gives us a way to utilize the results in the information theory to construct privacy channels. As some examples, it is used to construct several basic privacy channels, such as the random response privacy channel, the exponential privacy channel and the Gaussian privacy channel, which are respective counterparts of the random response mechanism, the exponential mechanism and the Gaussian mechanism of differential privacy.

Includes members:
  Member "on the information privacy model the group and composition privacy":
    also connected through other hyperedges:
      (none)
  Member "privacy loss in apple s implementation of differential privacy on macos 10 12":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "inferential privacy guarantees for differentially private mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentially private model publishing for deep learning")
  Member "max information differential privacy and post selection hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "generalization in adaptive data analysis and holdout reuse":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "principled evaluation of differentially private algorithms using dpbench":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally private hypothesis selection")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")

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)