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 "differential privacy at risk bridging randomness and privacy budget"):
  Abstract: The calibration of noise for a privacy-preserving mechanism depends on the sensitivity of the query and the prescribed privacy level. A data steward must make the non-trivial choice of a privacy level that balances the requirements of users and the monetary constraints of the business entity. We analyse roles of the sources of randomness, namely the explicit randomness induced by the noise distribution and the implicit randomness induced by the data-generation distribution, that are involved in the design of a privacy-preserving mechanism. The finer analysis enables us to provide stronger privacy guarantees with quantifiable risks. Thus, we propose privacy at risk that is a probabilistic calibration of privacy-preserving mechanisms. We provide a composition theorem that leverages privacy at risk. We instantiate the probabilistic calibration for the Laplace mechanism by providing analytical results. We also propose a cost model that bridges the gap between the privacy level and the compensation budget estimated by a GDPR compliant business entity. The convexity of the proposed cost model leads to a unique fine-tuning of privacy level that minimises the compensation budget. We show its effectiveness by illustrating a realistic scenario that avoids overestimation of the compensation budget by using privacy at risk for the Laplace mechanism. We quantitatively show that composition using the cost optimal privacy at risk provides stronger privacy guarantee than the classical advanced composition.

Includes members:
  Member "federated learning with bayesian differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "issues encountered deploying differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypothesis testing interpretations and renyi differential privacy")
  Member "pain free random differential privacy with sensitivity sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments")
  Member "concentrated differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privately learning markov random fields")
  Member "differential privacy an economic method for choosing epsilon":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "functional mechanism regression analysis under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack")
  Member "truthful mechanisms for agents that value privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "price of privacy in the keynesian beauty contest")
  Member "selling privacy at auction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "guidelines for implementing and auditing differentially private systems")

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)