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: "taming momentum in a distributed asynchronous environment"
  Abstract: Although distributed computing can significantly reduce the training time of deep neural networks, scaling the training process while maintaining high efficiency and final accuracy is challenging. Distributed asynchronous training enjoys near-linear speedup, but asynchrony causes gradient staleness, the main difficulty in scaling stochastic gradient descent to large clusters. Momentum, which is often used to accelerate convergence and escape local minima, exacerbates the gradient staleness, thereby hindering convergence. We propose DANA: a novel asynchronous distributed technique which is based on a new gradient staleness measure that we call the gap. By minimizing the gap, DANA mitigates the gradient staleness, despite using momentum, and therefore scales to large clusters while maintaining high final accuracy and fast convergence. DANA adapts Nesterov's Accelerated Gradient to a distributed setting, computing the gradient on an estimated future position of the model's parameters. In turn, we show that DANA's estimation of the future position amplifies the use of a Taylor expansion, which relies on a fast Hessian approximation, making it much more effective and accurate. Our evaluation on the CIFAR and ImageNet datasets shows that DANA outperforms existing methods, in both final accuracy and convergence speed.

Connected through hyperedge HE-1 (induced by source paper "gap aware mitigation of gradient staleness"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "making asynchronous stochastic gradient descent work for transformers"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting distributed synchronous sgd"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "asynchronous stochastic gradient descent with delay compensation"

Connected through hyperedge HE-2 (induced by source paper "at stability s edge how to adjust hyperparameters to preserve minima selection in asynchronous training of neural networks"):
  with members:
    - "a unified analysis of stochastic momentum methods for deep learning"
    - "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization"
    - "stochastic gradient push for distributed deep learning"
    - "step size matters in deep learning"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "wide residual networks"

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.LG(Machine Learning)