human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis")
  Member "univr a universal variance reduction framework for proximal stochastic gradient method":
    also connected through other hyperedges:
      (none)
  Member "new optimisation methods for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic doubly robust gradient")
  Member "sdca without duality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport")
  Member "improved svrg for non strongly convex or sum of non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variance reduction with sparse gradients")
  Member "a stochastic pca and svd algorithm with an exponential convergence rate":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning a single neuron with gradient methods")
  Member "finito a faster permutable incremental gradient method for big data problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")

Question: Please tell me which class the source paper 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)