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: "adabatch adaptive batch sizes for training deep neural networks"
  Abstract: Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size. While smaller batch sizes generally converge in fewer training epochs, larger batch sizes offer more parallelism and hence better computational efficiency. We have developed a new training approach that, rather than statically choosing a single batch size for all epochs, adaptively increases the batch size during the training process. Our method delivers the convergence rate of small batch sizes while achieving performance similar to large batch sizes. We analyse our approach using the standard AlexNet, ResNet, and VGG networks operating on the popular CIFAR-10, CIFAR-100, and ImageNet datasets. Our results demonstrate that learning with adaptive batch sizes can improve performance by factors of up to 6.25 on 4 NVIDIA Tesla P100 GPUs while changing accuracy by less than 1% relative to training with fixed batch sizes.

Connected through hyperedge HE-1 (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"
    - "an empirical model of large batch training"
    - "don t use large mini batches use local sgd"
    - "averaging weights leads to wider optima and better generalization"

Connected through hyperedge HE-2 (induced by source paper "geoda a geometric framework for black box adversarial attacks"):
  with members:
    - "a geometry inspired decision based attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "simple black box adversarial attacks"
    - "stateful detection of black box adversarial attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"

Connected through hyperedge HE-3 (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"
    - "an empirical model of large batch training"
    - "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"

Connected through hyperedge HE-4 (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"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "imagenet training in minutes"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-5 (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"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "inefficiency of k fac for large batch size training"):
  with members:
    - "parameter re initialization through cyclical batch size schedules"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "three mechanisms of weight decay regularization"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "large batch optimization for deep learning training bert in 76 minutes"):
  with members:
    - "large batch training for lstm and beyond"
    - "image classification at supercomputer scale"
    - "signsgd compressed optimisation for non convex problems"
    - "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"
    - "scaling sgd batch size to 32k for imagenet training"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-8 (induced by source paper "x traincaps accelerated training of capsule nets through lightweight software optimizations"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "dynamic routing between capsules"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "starting small learning with adaptive sample sizes"
    - "sgdr stochastic gradient descent with restarts"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "stop wasting my gradients practical svrg"
    - "no more pesky learning rate guessing games"

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)