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 "decoupled asynchronous proximal stochastic gradient descent with variance reduction"):
  Abstract: In the era of big data, optimizing large scale machine learning problems becomes a challenging task and draws significant attention. Asynchronous optimization algorithms come out as a promising solution. Recently, decoupled asynchronous proximal stochastic gradient descent (DAP-SGD) is proposed to minimize a composite function. It is claimed to be able to off-loads the computation bottleneck from server to workers by allowing workers to evaluate the proximal operators, therefore, server just need to do element-wise operations. However, it still suffers from slow convergence rate because of the variance of stochastic gradient is nonzero. In this paper, we propose a faster method, decoupled asynchronous proximal stochastic variance reduced gradient descent method (DAP-SVRG). We prove that our method has linear convergence for strongly convex problem. Large-scale experiments are also conducted in this paper, and results demonstrate our theoretical analysis.

Includes members:
  Member "asynchronous stochastic gradient descent with variance reduction for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive weight decay for deep neural networks")
  Member "mini batch semi stochastic gradient descent in the proximal setting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "asynchronous distributed semi stochastic gradient optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed momentum for byzantine resilient learning")
  Member "distributed mini batch sdca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic distributed and federated optimization for machine learning")
  Member "staleness aware async sgd for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts")

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)