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 empirical model of large batch training"
  Abstract: In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain. In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN). We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance. Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-3 (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"
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "a mean field theory of batch normalization"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "stochastic weight averaging in parallel large batch training that generalizes well"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "swalp stochastic weight averaging in low precision training"
    - "federated learning challenges methods and future directions"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "don t use large mini batches use local sgd"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-6 (induced by source paper "wide minima density hypothesis and the explore exploit learning rate schedule"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards understanding generalization in gradient based meta learning"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-7 (induced by source paper "seed rl scalable and efficient deep rl with accelerated central inference"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "torchbeast a pytorch platform for distributed rl"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "discovery of useful questions as auxiliary tasks"
    - "google research football a novel reinforcement learning environment"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "cross view policy learning for street navigation"
    - "neural replicator dynamics"

Connected through hyperedge HE-8 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "low latency video semantic segmentation"
    - "three factors influencing minima in sgd"
    - "slowfast networks for video recognition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "tvqa localized compositional video question answering"

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)