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: "coupling adaptive batch sizes with learning rates"
  Abstract: Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. #R##N#We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On popular image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.

Connected through hyperedge HE-1 (induced by source paper "adaptive regularization via residual smoothing in deep learning optimization"):
  with members:
    - "combining learning rate decay and weight decay with complexity gradient descent part i"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "unsupervised learning of depth and ego motion from video"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-2 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"

Connected through hyperedge HE-3 (induced by source paper "backpack packing more into backprop"):
  with members:
    - "a modular approach to block diagonal hessian approximations for second order optimization methods"
    - "deepobs a deep learning optimizer benchmark suite"
    - "limitations of the empirical fisher approximation"
    - "don t unroll adjoint differentiating ssa form programs"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "not all samples are created equal deep learning with importance sampling"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "efficient optimization of loops and limits with randomized telescoping sums"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "learned optimizers that outperform on wall clock and validation loss"
    - "understanding short horizon bias in stochastic meta optimization"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "truncated back propagation for bilevel optimization"
    - "unbiasing truncated backpropagation through time"
    - "an analysis of neural language modeling at multiple scales"
    - "learning longer term dependencies in rnns with auxiliary losses"

Connected through hyperedge HE-5 (induced by source paper "improving the convergence of sgd through adaptive batch sizes"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "understanding deep learning requires rethinking generalization"
    - "imagenet pre trained models with batch normalization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "big batch sgd automated inference using adaptive batch sizes"

Connected through hyperedge HE-6 (induced by source paper "active probabilistic inference on matrices for pre conditioning in stochastic optimization"):
  with members:
    - "quasi newton methods a new direction"

Connected through hyperedge HE-7 (induced by source paper "large batch size training of neural networks with adversarial training and second order information"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "oversketched newton fast convex optimization for serverless systems"
    - "pyhessian neural networks through the lens of the hessian"
    - "inefficiency of k fac for large batch size training"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-8 (induced by source paper "an empirical model of large batch training"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "understanding short horizon bias in stochastic meta optimization"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "accelerated methods for deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "gradient descent happens in a tiny subspace"
    - "measuring the effects of data parallelism on neural network training"
    - "imagenet training in minutes"

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)