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 "scaling up distributed stochastic gradient descent using variance reduction"):
  Abstract: Variance reduction stochastic gradient descent methods enable minimization of model fitting problems involving big datasets with low iteration complexity and fast asymptotic convergence rates. However, they scale poorly in distributed settings. In this paper, we propose a highly parallel variance reduction method, CentralVR, with performance that scales linearly with the number of worker nodes. We also propose distributed versions of popular variance reduction methods that support a high degree of parallelization. Unlike existing distributed stochastic gradient schemes, CentralVR exhibits linear performance gains up to thousands of cores for massive datasets.

Includes members:
  Member "importance sampling for minibatches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning")
  Member "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate")
  Member "mini batch semi stochastic gradient descent in the proximal setting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources")
  Member "online learning to sample":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets")
  Member "on variance reduction in stochastic gradient descent and its asynchronous variants":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "stop wasting my gradients practical svrg":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sample efficient policy gradient methods with recursive variance reduction")
  Member "finito a faster permutable incremental gradient method for big data problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "deep learning with elastic averaging sgd":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for federated learning")

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)