human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "is asgd accelerating asynchronous sgd using importance sampling"
  Abstract: Variance reduction (VR) techniques for convergence rate acceleration of stochastic gradient descent (SGD) algorithm have been developed with great efforts recently. VR's two variants, stochastic variance-reduced-gradient (SVRG-SGD) and importance sampling (IS-SGD) have achieved remarkable progresses. Meanwhile, asynchronous SGD (ASGD) is becoming more critical due to the ever-increasing scale of the optimization problems. The application of VR in ASGD to accelerate its convergence rate has therefore attracted much interest and SVRG-ASGDs are therefore proposed. However, we found that SVRG suffers dissatisfying performance in accelerating ASGD when the datasets are sparse and large-scale. In such case, SVRG-ASGD's iterative computation cost is magnitudes higher than ASGD which makes it very slow. On the other hand, IS achieves improved convergence rate with few extra computation cost and is invariant to the sparsity of dataset. This advantage makes it very suitable for the acceleration of ASGD for large-scale sparse datasets. In this paper we propose a novel IS-combined ASGD for effective convergence rate acceleration, namely, IS-ASGD. We theoretically prove the superior convergence bound of IS-ASGD. Experimental results also demonstrate our statements.

Connected through hyperedge HE-1 (induced by source paper "do optimization methods in deep learning applications matter"):
  with members:
    - "a survey of optimization methods from a machine learning perspective"
    - "quasi newton optimization methods for deep learning applications"
    - "deep reinforcement learning via l bfgs optimization"
    - "imagenet training in minutes"
    - "deep learning with elastic averaging sgd"

Question: Please tell me which class the center node 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.DC(Distributed, Parallel, and Cluster Computing)