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: "augment your batch better training with larger batches"
  Abstract: Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of deep neural networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently.

Connected through hyperedge HE-1 (induced by source paper "topologically densified distributions"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "a kernel theory of modern data augmentation"
    - "utilizing class information for deep network representation shaping"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a topological regularizer for classifiers via persistent homology"

Connected through hyperedge HE-2 (induced by source paper "mix match training convnets with mixed image sizes for improved accuracy speed and scale resiliency"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "faster neural network training with data echoing"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "yolov3 an incremental improvement"
    - "regularized evolution for image classifier architecture search"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-3 (induced by source paper "multigrain a unified image embedding for classes and instances"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "exploring the limits of weakly supervised pretraining"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "learning by cheating"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "does computer vision matter for action"
    - "monocular plan view networks for autonomous driving"
    - "objects as points"
    - "pytorch an imperative style high performance deep learning library"
    - "an algorithmic perspective on imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-5 (induced by source paper "occlusions for effective data augmentation in image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "normgrad finding the pixels that matter for training"
    - "dropblock a regularization method for convolutional networks"
    - "rise randomized input sampling for explanation of black box models"
    - "learning transferable architectures for scalable image recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-6 (induced by source paper "the knowledge within methods for data free model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "zero shot knowledge distillation in deep networks"
    - "how good is my gan"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-7 (induced by source paper "adversarial autoaugment"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "bag of tricks for image classification with convolutional neural networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "autoaugment learning augmentation policies from data"
    - "blockqnn efficient block wise neural network architecture generation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "faster neural network training with data echoing"):
  with members:
    - "image classification at supercomputer scale"
    - "not all samples are created equal deep learning with importance sampling"
    - "measuring the effects of data parallelism on neural network training"
    - "characterizing deep learning i o workloads in tensorflow"
    - "critical hyper parameters no random no cry"
    - "attention is all you need"
    - "faster sgd training by minibatch persistency"
    - "ssd single shot multibox detector"

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)