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: "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
  Abstract: Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns. In this work, we study how the critical batch size changes based on properties of the optimization algorithm, including acceleration and preconditioning, through two different lenses: large scale experiments, and analysis of a simple noisy quadratic model (NQM). We experimentally demonstrate that optimization algorithms that employ preconditioning, specifically Adam and K-FAC, result in much larger critical batch sizes than stochastic gradient descent with momentum. We also demonstrate that the NQM captures many of the essential features of real neural network training, despite being drastically simpler to work with. The NQM predicts our results with preconditioned optimizers, previous results with accelerated gradient descent, and other results around optimal learning rates and large batch training, making it a useful tool to generate testable predictions about neural network optimization.

Connected through hyperedge HE-1 (induced by source paper "scaling laws for neural language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "growing a brain fine tuning by increasing model capacity"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "a constructive prediction of the generalization error across scales"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "one epoch is all you need"

Connected through hyperedge HE-2 (induced by source paper "scalable and practical natural gradient for large scale deep learning"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "limitations of the empirical fisher approximation"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "information matrices and generalization"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "image classification at supercomputer scale"
    - "don t use large mini batches use local sgd"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-3 (induced by source paper "the break even point on optimization trajectories of deep neural networks"):
  with members:
    - "lca loss change allocation for neural network training"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "the local elasticity of neural networks"
    - "emergent properties of the local geometry of neural loss landscapes"

Connected through hyperedge HE-4 (induced by source paper "on empirical comparisons of optimizers for deep learning"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "namsg an efficient method for training neural networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "ellipsoidal trust region methods and the marginal value of hessian information for neural network training"

Connected through hyperedge HE-5 (induced by source paper "lookahead optimizer k steps forward 1 step back"):
  with members:
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "reptile a scalable metalearning algorithm"
    - "aggregated momentum stability through passive damping"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "adafactor adaptive learning rates with sublinear memory cost"

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)