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: "saags biased stochastic variance reduction methods"
  Abstract: Stochastic optimization is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients, and momentum acceleration. In this paper, we have proposed simple variants of SAAG-I and II (Stochastic Average Adjusted Gradient) \cite{Chauhan2017Saag}, called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch, respectively. To determine the step size, we introduce Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We also extend SAAGs (I, II, III, IV), to solve non-smooth problems and design two update rules for smooth and non-smooth problems. Moreover, our theoretical results prove linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies prove the efficacy of proposed methods against the state-of-art techniques, like, SVRG and VR-SGD.

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"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "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 "faster algorithms for large scale machine learning using simple sampling techniques"):
  with members:
    - "faster learning by reduction of data access time"
    - "importance sampling for minibatches"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"

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)