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 "a distributed hierarchical sgd algorithm with sparse global reduction"):
  Abstract: Reducing communication overhead is a big challenge for large-scale distributed training. To address this issue, we present a hierarchical averaging stochastic gradient descent (Hier-AVG) algorithm that reduces global reductions (averaging) by employing less costly local reductions. As a very general type of parallel SGD, Hier-AVG can reproduce several commonly adopted synchronous parallel SGD variants by adjusting its parameters. We establish standard convergence results of Hier-AVG for non-convex smooth optimization problems. Under the non-asymptotic scenario, we show that Hier-AVG with less frequent global averaging can sometimes have faster training speed. In addition, we show that more frequent local averaging with more participants involved can lead to faster training convergence. By comparing Hier-AVG with another distributed training algorithm K-AVG, we show that through deploying local averaging with less global averaging Hier-AVG can still achieve comparable training speed while constantly get better test accuracy. As a result, local averaging can serve as an alternative remedy to effectively reduce communication overhead when the number of learners is large. We test Hier-AVG with several state-of-the-art deep neural nets on CIFAR-10 to validate our analysis. Further experiments to compare Hier-AVG with K-AVG on ImageNet-1K also show Hier-AVG's superiority over K-AVG.

Includes members:
  Member "adaptive communication strategies to achieve the best error runtime trade off in local update sgd":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "don t use large mini batches use local sgd":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "revisiting distributed synchronous sgd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "memory and communication efficient distributed stochastic optimization with minibatch prox":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "is local sgd better than minibatch sgd")
  Member "cocoa a general framework for communication efficient distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "sgdr stochastic gradient descent with restarts":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)