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: "sdca without duality regularization and individual convexity"
  Abstract: Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. We describe variants of SDCA that do not require explicit regularization and do not rely on duality. We prove linear convergence rates even if individual loss functions are non-convex, as long as the expected loss is strongly convex.

Connected through hyperedge HE-1 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions"):
  with members:
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "information based complexity feedback and dynamics in convex programming"

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 "supervised learning under distributed features"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"
    - "distributed learning for stochastic generalized nash equilibrium problems"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-4 (induced by source paper "sgd general analysis and improved rates"):
  with members:
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stochastic gradient push for distributed deep learning"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "importance sampling for minibatches"
    - "train faster generalize better stability of stochastic gradient descent"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "direct acceleration of saga using sampled negative momentum"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "variance reduced stochastic gradient descent with neighbors"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"

Connected through hyperedge HE-6 (induced by source paper "saga with arbitrary sampling"):
  with members:
    - "importance sampling for minibatches"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-7 (induced by source paper "stochastic variance reduced cubic regularized newton method"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a general and adaptive robust loss function"
    - "sub sampled cubic regularization for non convex optimization"
    - "matrix completion has no spurious local minimum"
    - "a variance reduced stochastic newton method"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "katyusha x practical momentum method for stochastic sum of nonconvex optimization"):
  with members:
    - "how to make the gradients small stochastically"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

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)