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: "almost tune free variance reduction"
  Abstract: The variance reduction class of algorithms including the representative ones, abbreviated as SVRG and SARAH, have well documented merits for empirical risk minimization tasks. However, they require grid search to optimally tune parameters (step size and the number of iterations per inner loop) for best performance. This work introduces `almost tune-free' SVRG and SARAH schemes by equipping them with Barzilai-Borwein (BB) step sizes. To achieve the best performance, both i) averaging schemes; and, ii) the inner loop length are adjusted according to the BB step size. SVRG and SARAH are first reexamined through an `estimate sequence' lens. Such analysis provides new averaging methods that tighten the convergence rates of both SVRG and SARAH theoretically, and improve their performance empirically when the step size is chosen large. Then a simple yet effective means of adjusting the number of iterations per inner loop is developed, which completes the tune-free variance reduction together with BB step sizes. Numerical tests corroborate the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "adaptive step sizes in variance reduction via regularization"):
  with members:
    - "accelerating mini batch sarah by step size rules"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the convergence of adam and beyond"
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "on the convergence of sarah and beyond"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of sarah and beyond"):
  with members:
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "how to escape saddle points efficiently"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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)