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: "improved svrg for non strongly convex or sum of non convex objectives"
  Abstract: Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings. In this paper, we show that SVRG is one such method: being originally designed for strongly convex objectives, it is also very robust in non-strongly convex or sum-of-non-convex settings. #R##N#More precisely, we provide new analysis to improve the state-of-the-art running times in both settings by either applying SVRG or its novel variant. Since non-strongly convex objectives include important examples such as Lasso or logistic regression, and sum-of-non-convex objectives include famous examples such as stochastic PCA and is even believed to be related to training deep neural nets, our results also imply better performances in these applications.

Connected through hyperedge HE-1 (induced by source paper "variance reduction with sparse gradients"):
  with members:
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "katyusha x practical momentum method for stochastic sum of nonconvex optimization"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "sparsified sgd with memory"
    - "don t decay the learning rate increase the batch size"
    - "sparse communication for distributed gradient descent"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"

Connected through hyperedge HE-3 (induced by source paper "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"):
  with members:
    - "riemannian stochastic quasi newton algorithm with variance reduction and its convergence analysis"
    - "riemannian stochastic variance reduced gradient on grassmann manifold"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "sdca without duality"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-4 (induced by source paper "asvrg accelerated proximal svrg"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "accelerated variance reduced stochastic admm"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "mini batch semi stochastic gradient descent in the proximal setting"

Connected through hyperedge HE-5 (induced by source paper "solving approximate wasserstein gans to stationarity"):
  with members:
    - "improving gans using optimal transport"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-6 (induced by source paper "zeroth order stochastic variance reduction for nonconvex optimization"):
  with members:
    - "bandit convex optimization for scalable and dynamic iot management"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "practical black box attacks against machine learning"
    - "zeroth order asynchronous doubly stochastic algorithm with variance reduction"
    - "towards evaluating the robustness of neural networks"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"

Connected through hyperedge HE-7 (induced by source paper "vr sgd a simple stochastic variance reduction method for machine learning"):
  with members:
    - "accelerated variance reduced stochastic admm"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "variance reduced and projection free stochastic optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "deep learning with elastic averaging sgd"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "differentially private empirical risk minimization revisited faster and more general"):
  with members:
    - "private incremental regression"
    - "the price of differential privacy for online learning"
    - "beyond convexity stochastic quasi convex optimization"
    - "revisiting differentially private regression lessons from learning theory and their consequences"
    - "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private online learning"
    - "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)