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: "vr sgd a simple stochastic variance reduction method for machine learning"
  Abstract: In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from its counterparts that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate $\mathcal{O}(1/T^2)$. Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, and leading eigenvalue computation. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha.

Connected through hyperedge HE-1 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning"):
  with members:
    - "saags biased stochastic variance reduction methods for large scale learning"
    - "libs2ml a library for scalable second order machine learning algorithms"
    - "saags biased stochastic variance reduction methods"
    - "faster learning by reduction of data access time"
    - "importance sampling for minibatches"
    - "a variance reduced stochastic newton method"
    - "res regularized stochastic bfgs algorithm"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "saags biased stochastic variance reduction methods for large scale learning"):
  with members:
    - "stochastic trust region inexact newton method for large scale machine learning"
    - "faster learning by reduction of data access time"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "randomized block coordinate descent for online and stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "asvrg accelerated proximal svrg"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "accelerated variance reduced stochastic admm"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "an overview of gradient descent optimization algorithms"

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)