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"
  Abstract: Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. In this paper we show how a variant of SDCA can be applied for non-convex losses. We prove linear convergence rate even if individual loss functions are non-convex as long as the expected loss is convex.

Connected through hyperedge HE-1 (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"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "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-2 (induced by source paper "stochastic nested variance reduction for nonconvex optimization"):
  with members:
    - "katyusha x practical momentum method for stochastic sum of nonconvex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-4 (induced by source paper "distributed asynchronous dual free stochastic dual coordinate ascent"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "distributed mini batch sdca"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-5 (induced by source paper "faster eigenvector computation via shift and invert preconditioning"):
  with members:
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "primal dual rates and certificates"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "distributed optimization with arbitrary local solvers"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "mini batch primal and dual methods for svms"
    - "an equivalence between the lasso and support vector machines"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-7 (induced by source paper "sdca without duality regularization and individual convexity"):
  with members:
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "univr a universal variance reduction framework for proximal stochastic gradient method"
    - "new optimisation methods for machine learning"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "importance sampling for minibatches"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "stop wasting my gradients practical svrg"
    - "semi stochastic coordinate descent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"
    - "hybrid deterministic stochastic methods for data fitting"

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)