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 "deep learning with gaussian differential privacy"):
  Abstract: Deep learning models are often trained on datasets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential privacy or its divergence-based relaxations. These privacy definitions, however, have weaknesses in handling certain important primitives (composition and subsampling), thereby giving loose or complicated privacy analyses of training neural networks. In this paper, we consider a recently proposed privacy definition termed f-differential privacy [17] for a refined privacy analysis of training neural networks. Leveraging the appealing properties of f-differential privacy in handling composition and subsampling, this paper derives analytically tractable expressions for the privacy guarantees of both stochastic gradient descent and Adam used in training deep neural networks, without the need of developing sophisticated techniques as [3] did. Our results demonstrate that the f-differential privacy framework allows for a new privacy analysis that improves on the prior analysis [3], which in turn suggests tuning certain parameters of neural networks for a better prediction accuracy without violating the privacy budget. These theoretically derived improvements are confirmed by our experiments in a range of tasks in image classification, text classification, and recommender systems.

Includes members:
  Member "hypothesis testing interpretations and renyi differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences")
  Member "adaclip adaptive clipping for private sgd":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "removing disparate impact of differentially private stochastic gradient descent on model accuracy")
  Member "gaussian differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypothesis testing interpretations and renyi differential privacy")
  Member "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale")
  Member "a general approach to adding differential privacy to iterative training procedures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "efficient private erm for smooth objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")
  Member "collecting telemetry data privately":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "concentrated differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privately learning markov random fields")

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)