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: "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
  Abstract: Large-scale distributed training of deep neural networks suffer from the generalization gap caused by the increase in the effective mini-batch size. Previous approaches try to solve this problem by varying the learning rate and batch size over epochs and layers, or some ad hoc modification of the batch normalization. We propose an alternative approach using a second-order optimization method that shows similar generalization capability to first-order methods, but converges faster and can handle larger mini-batches. To test our method on a benchmark where highly optimized first-order methods are available as references, we train ResNet-50 on ImageNet. We converged to 75% Top-1 validation accuracy in 35 epochs for mini-batch sizes under 16,384, and achieved 75% even with a mini-batch size of 131,072, which took only 978 iterations.

Connected through hyperedge HE-1 (induced by source paper "scalable and practical natural gradient for large scale deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "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"

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 "large batch training for lstm and beyond"):
  with members:
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"
    - "imagenet training in minutes"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-4 (induced by source paper "reducing bert pre training time from 3 days to 76 minutes"):
  with members:
    - "large batch training for lstm and beyond"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"
    - "imagenet training in minutes"

Connected through hyperedge HE-5 (induced by source paper "one epoch is all you need"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "data efficient image recognition with contrastive predictive coding"
    - "generating long sequences with sparse transformers"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "a simple method for commonsense reasoning"
    - "training tips for the transformer model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-6 (induced by source paper "gradient noise convolution gnc smoothing loss function for distributed large batch sgd"):
  with members:
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "an alternative view when does sgd escape local minima"
    - "imagenet training in minutes"
    - "random erasing data augmentation"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-7 (induced by source paper "dc s3gd delay compensated stale synchronous sgd for large scale decentralized neural network training"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "an empirical model of large batch training"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "visualizing the loss landscape of neural nets"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-8 (induced by source paper "limitations of the empirical fisher approximation"):
  with members:
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"
    - "riemannian metrics for neural networks i feedforward 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)