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 "exploiting the structure stochastic gradient methods using raw clusters"):
  Abstract: The amount of data available in the world is growing faster and bigger than our ability to deal with it. However, if we take advantage of the internal structure, data may become much smaller for machine learning purposes. In this paper we focus on one of the most fundamental machine learning tasks, empirical risk minimization (ERM), and provide faster algorithms with the help from the clustering structure of the data. #R##N#We introduce a simple notion of raw clustering that can be efficiently obtained with just one pass of the data, and propose two algorithms. Our variance-reduction based algorithm ClusterSVRG introduces a new gradient estimator using the clustering information, and our accelerated algorithm ClusterACDM is built on a novel Haar transformation applied to the dual space of each cluster. Our algorithms outperform their classical counterparts both in theory and practice.

Includes members:
  Member "on variance reduction in stochastic gradient descent and its asynchronous variants":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "variance reduced stochastic gradient descent with neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "federated learning of a mixture of global and local models")
  Member "finito a faster permutable incremental gradient method for big data problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "on data preconditioning for regularized loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solving ridge regression using sketched preconditioned svrg")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral sparsification via bounded independence sampling")

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)