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 "evaluating differentially private machine learning in practice"):
  Abstract: Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $\epsilon$, about how much information is leaked by a mechanism. When used in privacy-preserving machine learning, the goal is typically to limit what can be inferred from the model about individual training records. However, the calibration of the privacy budget is not well understood. Implementations of privacy-preserving machine learning often select large values of $\epsilon$ in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, relaxed definitions of differential privacy are often used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is no way to obtain privacy for free---relaxed definitions of differential privacy that reduce the amount of noise needed to improve utility also increase the measured privacy leakage. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs for complex learning tasks: settings that provide limited accuracy loss provide little effective privacy, and settings that provide strong privacy result in useless models. The paper has been accepted at USENIX Security 2019. Open source code for reproducing all of our experiments is available at this https URL.

Includes members:
  Member "differentially private model publishing for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "membership inference attacks against object detection models")
  Member "privacy preserving collaborative deep learning with irregular participants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "preserving differential privacy in adversarial learning with provable robustness")
  Member "cache telepathy leveraging shared resource attacks to learn dnn architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints")
  Member "differentially private empirical risk minimization revisited faster and more general":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "input perturbation a new paradigm between central and local differential privacy")
  Member "dp admm admm based distributed learning with differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance analysis and optimization in privacy preserving federated learning")
  Member "how to backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "ml leaks model and data independent membership inference attacks and defenses on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "efficient deep learning on multi source private data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")

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.LG(Machine Learning)