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 "variance reduction for distributed stochastic gradient descent"):
  Abstract: Variance reduction (VR) methods boost the performance of stochastic gradient descent (SGD) by enabling the use of larger stepsizes and preserving linear convergence rates. However, current variance reduced SGD methods require either high memory usage or require a full pass over the (large) data set at the end of each epoch to calculate the exact gradient of the objective function. This makes current VR methods impractical in distributed or parallel settings. In this paper, we propose a variance reduction method, called VR-lite, that does not require full gradient computations or extra storage. We explore distributed synchronous and asynchronous variants with both high and low communication latency. We find that our distributed algorithms scale linearly with the number of local workers and remain stable even with low communication frequency. We empirically compare both the sequential and distributed algorithms to state-of-the-art stochastic optimization methods, and find that our proposed algorithms consistently converge faster than other stochastic methods.

Includes members:
  Member "mini batch semi stochastic gradient descent in the proximal setting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources")
  Member "on variance reduction in stochastic gradient descent and its asynchronous variants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster on device training using new federated momentum algorithm")
  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 "deep learning with elastic averaging sgd":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  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")

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)