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 online learning"
  Abstract: In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy measure. For this problem, we distill two critical attributes that a private OCP algorithm should have in order to provide reasonable privacy as well as utility guarantees: 1) linearly decreasing sensitivity, i.e., as new data points arrive their effect on the learning model decreases, 2) sub-linear regret bound---regret bound is a popular goodness/utility measure of an online learning algorithm. #R##N#Given an OCP algorithm that satisfies these two conditions, we provide a general framework to convert the given algorithm into a privacy preserving OCP algorithm with good (sub-linear) regret. We then illustrate our approach by converting two popular online learning algorithms into their differentially private variants while guaranteeing sub-linear regret ($O(\sqrt{T})$). Next, we consider the special case of online linear regression problems, a practically important class of online learning problems, for which we generalize an approach by Dwork et al. to provide a differentially private algorithm with just $O(\log^{1.5} T)$ regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for offline learning as well. For the offline learning problem, our approach obtains better error bounds as well as can handle larger class of problems than the existing state-of-the-art methods Chaudhuri et al.

Connected through hyperedge HE-1 (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"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "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"

Connected through hyperedge HE-2 (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"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "private stochastic convex optimization with optimal rates"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-4 (induced by source paper "dynamically protecting privacy under uncertainty"):
  with members:
    - "query complexity of bayesian private learning"
    - "private sequential learning"
    - "quantifying surveillance in the networked age node based intrusions and group privacy"
    - "perception games and privacy"
    - "riposte an anonymous messaging system handling millions of users"
    - "spy vs spy rumor source obfuscation"
    - "the empirical implications of privacy aware choice"
    - "privacy preserving public information for sequential games"

Connected through hyperedge HE-5 (induced by source paper "differentially private survival function estimation"):
  with members:
    - "differentially private releasing via deep generative model technical report"
    - "generating differentially private datasets using gans"
    - "differentially private generative adversarial network"
    - "differentially private regression for discrete time survival analysis"
    - "differentially private ordinary least squares"
    - "calibrating data to sensitivity in private data analysis"
    - "functional mechanism regression analysis under differential privacy"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "privacy and utility tradeoff in approximate differential privacy"):
  with members:
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "locally private hypothesis testing"
    - "renyi differential privacy"
    - "dp em differentially private expectation maximization"
    - "calibrating data to sensitivity in private data analysis"
    - "universally optimal privacy mechanisms for minimax agents"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving multi party contextual bandits"):
  with members:
    - "towards federated learning at scale system design"
    - "privacy preserving classification of personal text messages with secure multi party computation an application to hate speech detection"
    - "differentially private contextual linear bandits"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "efficient deep learning on multi source private data"
    - "gazelle a low latency framework for secure neural network inference"
    - "privacy risk in machine learning analyzing the connection to overfitting"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving contextual bandits"):
  with members:
    - "towards federated learning at scale system design"
    - "efficient deep learning on multi source private data"
    - "gazelle a low latency framework for secure neural network inference"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "a contextual bandit approach to personalized news article recommendation"

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)