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 simple algorithm for estimating distribution parameters from n dimensional randomized binary responses"):
  Abstract: Randomized response for privacy protection is attractive as provided disclosure control can be quantified by means such as differential privacy. However, recovering statistics involving multiple dependent binary attributes can be difficult, posing a barrier to the use of randomized response for privacy protection. In this work, we identify a family of randomizers for which we are able to present a simple and efficient algorithm for obtaining unbiased maximum likelihood estimates for k-way marginal distributions from the randomized data. We also provide theoretical bounds on the statistical efficiency of these estimates, allowing the assessment of sample sizes for these randomizers. The identified family consists of randomizers generated by an iterated Kronecker product of an invertible and bisymmetric 2 x 2 matrix. This family includes modes of Google's Rappor randomizer, as well as applications of two well-known classical randomized response methods: Warner's original method, and Simmons' unrelated question method. We find that randomizers in this family can also be considered to be equivalent to each other with respect to the efficiency -- differential privacy tradeoff. Importantly, the estimation algorithm is simple to implement, an aspect critical to technologies for privacy protection and security.

Includes members:
  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 "building a rappor with the unknown privacy preserving learning of associations and data dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless federated learning with local differential privacy")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "extremal mechanisms for local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  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")

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)