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: "an exponential learning rate schedule for deep learning"
  Abstract: Intriguing empirical evidence exists that deep learning can work well with exoticschedules for varying the learning rate. This paper suggests that the phenomenonmay be due to Batch Normalization or BN(Ioffe & Szegedy, 2015), which is ubiquitous and provides benefits in optimization and generalization across all standardarchitectures. The following new results are shown about BN with weight decay and momentum (in other words, the typical use case which was not considered inearlier theoretical analyses of stand-alone BN (Ioffe & Szegedy, 2015; Santurkaret al., 2018; Arora et al., 2018). #R##N#1. Training can be done using SGD with momentum and an exponentially increasing learning rate schedule, i.e., learning rate increases by some $(1 +\alpha)$ factor in every epoch for some $\alpha >0$. (Precise statement in the paper.) To the best of our knowledge this is the first time such a rate schedule has been successfully used, let alone for highly successful architectures. As expected, such training rapidly blows up network weights, but the net stays well-behaved due to normalization. #R##N#2. Mathematical explanation of the success of the above rate schedule: a rigorous proof that it is equivalent to the standard setting of BN + SGD + StandardRate Tuning + Weight Decay + Momentum. This equivalence holds for other normalization layers as well, Group Normalization(Wu & He, 2018), LayerNormalization(Ba et al., 2016), Instance Norm(Ulyanov et al., 2016), etc. #R##N#3. A worked-out toy example illustrating the above linkage of hyper-parameters. Using either weight decay or BN alone reaches global minimum, but convergence fails when both are used.

Connected through hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "deepobs a deep learning optimizer benchmark suite"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-2 (induced by source paper "the two regimes of deep network training"):
  with members:
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "measuring the effects of data parallelism on neural network training"
    - "super convergence very fast training of residual networks using large learning rates"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "large scale learning of general visual representations for transfer"):
  with members:
    - "self supervised learning of video induced visual invariances"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "billion scale semi supervised learning for image classification"
    - "momentum contrast for unsupervised visual representation learning"
    - "do we train on test data purging cifar of near duplicates"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"

Connected through hyperedge HE-4 (induced by source paper "linear mode connectivity and the lottery ticket hypothesis"):
  with members:
    - "the early phase of neural network training"
    - "picking winning tickets before training by preserving gradient flow"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "what s hidden in a randomly weighted neural network"
    - "the state of sparsity in deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural 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)