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: "differentially private empirical risk minimization revisited faster and more general"
  Abstract: In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings. For smooth (strongly) convex loss function with or without (non)-smooth regularization, we give algorithms that achieve either optimal or near optimal utility bounds with less gradient complexity compared with previous work. For ERM with smooth convex loss function in high-dimensional ($p\gg n$) setting, we give an algorithm which achieves the upper bound with less gradient complexity than previous ones. At last, we generalize the expected excess empirical risk from convex loss functions to non-convex ones satisfying the Polyak-Lojasiewicz condition and give a tighter upper bound on the utility than the one in \cite{ijcai2017-548}.

Connected through hyperedge HE-1 (induced by source paper "input perturbation a new paradigm between central and local differential privacy"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "differentially private bayesian linear regression"
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "efficient private erm for smooth objectives"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "differentially private erm based on data perturbation"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "differentially private bayesian linear regression"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "efficient private erm for smooth objectives"
    - "collect at once use effectively making non interactive locally private learning possible"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "differentially private model publishing for deep learning"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-4 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"
    - "detecting violations of differential privacy"

Connected through hyperedge HE-5 (induced by source paper "data dependent differentially private parameter learning for directed graphical models"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "differentially private bayesian inference for exponential families"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "dp em differentially private expectation maximization"
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "understanding deep learning requires rethinking generalization"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-6 (induced by source paper "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"):
  with members:
    - "efficient privacy preserving nonconvex optimization"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "laplacian smoothing gradient descent"
    - "differentially private federated learning a client level perspective"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "local differential privacy for physical sensor data and sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"):
  with members:
    - "dropblock a regularization method for convolutional networks"
    - "deja vu an empirical evaluation of the memorization properties of convnets"
    - "differentially private federated learning a client level perspective"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "efficient private erm for smooth objectives"
    - "detecting cancer metastases on gigapixel pathology images"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-8 (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"
    - "privacy amplification by iteration"
    - "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"

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)