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 "gradient perturbation is underrated for differentially private convex optimization"):
  Abstract: Gradient perturbation, widely used for differentially private optimization, injects noise at every iterative update to guarantee differential privacy. Previous work first determines the noise level that can satisfy the privacy requirement and then analyzes the utility of noisy gradient updates as in non-private case. In this paper, we explore how the privacy noise affects the optimization property. We show that for differentially private convex optimization, the utility guarantee of both DP-GD and DP-SGD is determined by an \emph{expected curvature} rather than the minimum curvature. The \emph{expected curvature} represents the average curvature over the optimization path, which is usually much larger than the minimum curvature and hence can help us achieve a significantly improved utility guarantee. By using the \emph{expected curvature}, our theory justifies the advantage of gradient perturbation over other perturbation methods and closes the gap between theory and practice. Extensive experiments on real world datasets corroborate our theoretical findings.

Includes members:
  Member "concentrated differentially private gradient descent with adaptive per iteration privacy budget":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis and optimization in privacy preserving federated learning")
  Member "differentially private empirical risk minimization revisited faster and more general":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "input perturbation a new paradigm between central and local differential privacy")
  Member "privacy amplification by iteration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences")
  Member "efficient private erm for smooth objectives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")
  Member "renyi differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "collecting telemetry data privately":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally private hypothesis selection")
  Member "deep models under the gan information leakage from collaborative deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "bolt on differential privacy for scalable stochastic gradient descent based analytics":
    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.LG(Machine Learning)