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: "efficient private erm for smooth objectives"
  Abstract: In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\epsilon$-DP and $(\epsilon, \delta)$-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time.

Connected through hyperedge HE-1 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"):
  with members:
    - "robust and communication efficient collaborative learning"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "an exact quantized decentralized gradient descent algorithm"
    - "communication compression for decentralized training"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "leasgd an efficient and privacy preserving decentralized algorithm for distributed learning"
    - "gradient sparsification for communication efficient distributed optimization"
    - "renyi differential privacy"

Connected through hyperedge HE-2 (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"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (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"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "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-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"
    - "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-5 (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-6 (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"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "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"
    - "detecting cancer metastases on gigapixel pathology images"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-7 (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"
    - "privacy amplification by iteration"
    - "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-8 (induced by source paper "efficient privacy preserving nonconvex optimization"):
  with members:
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "differentially private generative adversarial network"
    - "renyi differential privacy"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

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)