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 "asynchronous stochastic block coordinate descent with variance reduction"):
  Abstract: Asynchronous parallel implementations for stochastic optimization have received huge successes in theory and practice recently. Asynchronous implementations with lock-free are more efficient than the one with writing or reading lock. In this paper, we focus on a composite objective function consisting of a smooth convex function $f$ and a block separable convex function, which widely exists in machine learning and computer vision. We propose an asynchronous stochastic block coordinate descent algorithm with the accelerated technology of variance reduction (AsySBCDVR), which are with lock-free in the implementation and analysis. AsySBCDVR is particularly important because it can scale well with the sample size and dimension simultaneously. We prove that AsySBCDVR achieves a linear convergence rate when the function $f$ is with the optimal strong convexity property, and a sublinear rate when $f$ is with the general convexity. More importantly, a near-linear speedup on a parallel system with shared memory can be obtained.

Includes members:
  Member "asynchronous stochastic proximal optimization algorithms with variance reduction":
    also connected through other hyperedges:
      (none)
  Member "distributed asynchronous dual free stochastic dual coordinate ascent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "sdca without duality regularization and individual convexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions")
  Member "decoupled asynchronous proximal stochastic gradient descent with variance reduction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asynchronous stochastic gradient descent with variance reduction for non convex optimization")
  Member "asynchronous stochastic gradient descent with variance reduction for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive weight decay for deep neural networks")
  Member "passcode parallel asynchronous stochastic dual co ordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning")
  Member "on variance reduction in stochastic gradient descent and its asynchronous variants":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "revisiting asynchronous linear solvers provable convergence rate through randomization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed machine learning on mobile devices a survey")

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)