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 "implementing the exponential mechanism with base 2 differential privacy"):
  Abstract: Despite excellent theoretical support, Differential Privacy (DP) can still be a challenge to implement in practice. In part, this challenge is due to the very real concerns associated with converting arbitrary or infinite-precision theoretical mechanisms to the often messy realities of floating point or fixed-precision. Beginning with the troubling result of Mironov demonstrating the security issues of using floating point for implementing the Laplace mechanism, there have been many reasonable concerns raised on the vulnerabilities of real-world implementations of DP. In this work, we examine the practicalities of implementing the exponential mechanism of McSherry and Talwar. We demonstrate that naive or malicious implementations can result in catastrophic privacy failures. To address these problems, we show that the mechanism can be implemented exactly for a rich set of values of the privacy parameter $\varepsilon$ and utility functions with limited practical overhead in running time and minimal code complexity. How do we achieve this result? We employ a simple trick of switching from base $e$ to base 2, allowing us to perform precise base 2 arithmetic. A short, precise expression is always available for $\varepsilon$, and the only approximation error we incur is the conversion of the base-2 privacy parameter back to base $e$ for reporting purposes. The core base 2 arithmetic of the mechanism can be simply and efficiently implemented using open-source high precision floating point libraries. Furthermore, the exact nature of the implementation lends itself to simple monitoring of correctness and proofs of privacy.

Includes members:
  Member "differential privacy on finite computers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "preserving differential privacy under finite precision semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differential privacy techniques for cyber physical systems a survey")

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)