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: "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
  Abstract: The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical risk minimization paradigm. A key structural element in both of these methods is the inclusion of an outer loop at the beginning of which a full pass over the training data is made in order to compute the exact gradient, which is then used to construct a variance-reduced estimator of the gradient. In this work we design {\em loopless variants} of both of these methods. In particular, we remove the outer loop and replace its function by a coin flip performed in each iteration designed to trigger, with a small probability, the computation of the gradient. We prove that the new methods enjoy the same superior theoretical convergence properties as the original methods. However, we demonstrate through numerical experiments that our methods have substantially superior practical behavior.

Connected through hyperedge HE-1 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "adaptive gradient based meta learning methods"
    - "advances and open problems in federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "variational federated multi task learning"
    - "variance reduced local sgd with lower communication complexity"
    - "semi cyclic stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization"):
  with members:
    - "variance reduction with sparse gradients"
    - "stochastic newton and cubic newton methods with simple local linear quadratic rates"
    - "sgd general analysis and improved rates"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "stochastic cubic regularization for fast nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "stochastic newton and cubic newton methods with simple local linear quadratic rates"):
  with members:
    - "sgd general analysis and improved rates"
    - "how good is sgd with random shuffling"
    - "stochastic variance reduced cubic regularized newton method"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "sub sampled cubic regularization for non convex optimization"
    - "a proximal stochastic quasi newton algorithm"
    - "variance reduced second order methods"

Connected through hyperedge HE-4 (induced by source paper "adaptive step sizes in variance reduction via regularization"):
  with members:
    - "accelerating mini batch sarah by step size rules"
    - "almost tune free variance reduction"
    - "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-5 (induced by source paper "on the convergence of sarah and beyond"):
  with members:
    - "almost tune free variance reduction"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "almost tune free variance reduction"):
  with members:
    - "accelerating mini batch sarah by step size rules"
    - "on the convergence of sarah and beyond"
    - "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)