human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "privacy amplification by iteration"
  Abstract: Many commonly used learning algorithms work by iteratively updating an intermediate solution using one or a few data points in each iteration. Analysis of differential privacy for such algorithms often involves ensuring privacy of each step and then reasoning about the cumulative privacy cost of the algorithm. This is enabled by composition theorems for differential privacy that allow releasing of all the intermediate results. In this work, we demonstrate that for contractive iterations, not releasing the intermediate results strongly amplifies the privacy guarantees. We describe several applications of this new analysis technique to solving convex optimization problems via noisy stochastic gradient descent. For example, we demonstrate that a relatively small number of non-private data points from the same distribution can be used to close the gap between private and non-private convex optimization. In addition, we demonstrate that we can achieve guarantees similar to those obtainable using the privacy-amplification-by-sampling technique in several natural settings where that technique cannot be applied.

Connected through hyperedge HE-1 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences"):
  with members:
    - "hypothesis testing interpretations and renyi differential privacy"
    - "privacy amplification by mixing and diffusion mechanisms"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "renyi differential privacy"
    - "f divergence inequalities"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-2 (induced by source paper "secure federated submodel learning"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "deep leakage from gradients"
    - "towards federated learning at scale system design"
    - "making ai forget you data deletion in machine learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "consistent and accurate frequency oracles under local differential privacy"

Connected through hyperedge HE-3 (induced by source paper "blender enabling local search with a hybrid differential privacy model"):
  with members:
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
    - "the power of the hybrid model for mean estimation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "differentially private identity and closeness testing of discrete distributions"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-4 (induced by source paper "the cost of a reductions approach to private fair optimization"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "a reductions approach to fair classification"
    - "multi objective bandits optimizing the generalized gini index"
    - "equality of opportunity in supervised learning"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "between pure and approximate differential privacy"

Connected through hyperedge HE-5 (induced by source paper "the privacy blanket of the shuffle model"):
  with members:
    - "the role of interactivity in local differential privacy"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "distributed differential privacy via shuffling"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"
    - "proving differential privacy via probabilistic couplings"

Connected through hyperedge HE-6 (induced by source paper "gradient perturbation is underrated for differentially private convex optimization"):
  with members:
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-7 (induced by source paper "privacy amplification by mixing and diffusion mechanisms"):
  with members:
    - "the privacy blanket of the shuffle model"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "distributed differential privacy via shuffling"
    - "differentially private release and learning of threshold functions"
    - "characterizing the sample complexity of private learners"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"

Connected through hyperedge HE-8 (induced by source paper "amplifying r enyi differential privacy via shuffling"):
  with members:
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "differentially private empirical risk minimization"

Question: Please tell me which class the center node 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)