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 batch size training of neural networks with adversarial training and second order information"
  Abstract: The most straightforward method to accelerate Stochastic Gradient Descent (SGD) computation is to distribute the randomly selected batch of inputs over multiple processors. To keep the distributed processors fully utilized requires commensurately growing the batch size. However, large batch training often leads to poorer generalization. A recently proposed solution for this problem is to use adaptive batch sizes in SGD. In this case, one starts with a small number of processes and scales the processes as training progresses. Two major challenges with this approach are (i) that dynamically resizing the cluster can add non-trivial overhead, in part since it is currently not supported, and (ii) that the overall speed up is limited by the initial phase with smaller batches. In this work, we address both challenges by developing a new adaptive batch size framework, with autoscaling based on the Ray framework. This allows very efficient elastic scaling with negligible resizing overhead (0.32\% of time for ResNet18 ImageNet training). Furthermore, we propose a new adaptive batch size training scheme using second order methods and adversarial training. These enable increasing batch sizes earlier during training, which leads to better training time. We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple neural networks, including ResNets and smaller networks such as SqueezeNext. Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively). Importantly, this is achieved without any additional hyper-parameter tuning to tailor our method in any of these experiments.

Connected through hyperedge HE-1 (induced by source paper "hypersched dynamic resource reallocation for model development on a deadline"):
  with members:
    - "massively parallel hyperparameter tuning"
    - "an argument in favor of strong scaling for deep neural networks with small datasets"
    - "tiresias predicting security events through deep learning"
    - "tune a research platform for distributed model selection and training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "slaq quality driven scheduling for distributed machine learning"
    - "deep reinforcement learning that matters"
    - "rllib abstractions for distributed reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "hawq hessian aware quantization of neural networks with mixed precision"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-3 (induced by source paper "hawq v2 hessian aware trace weighted quantization of neural networks"):
  with members:
    - "q bert hessian based ultra low precision quantization of bert"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "trust region based adversarial attack on neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"

Connected through hyperedge HE-4 (induced by source paper "pyhessian neural networks through the lens of the hessian"):
  with members:
    - "hawq v2 hessian aware trace weighted quantization of neural networks"
    - "q bert hessian based ultra low precision quantization of bert"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "inefficiency of k fac for large batch size training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "eigenvalues of the hessian in deep learning singularity and beyond"

Connected through hyperedge HE-5 (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"
    - "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"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-6 (induced by source paper "topology aware deep learning for wireless network optimization"):
  with members:
    - "a graph neural network approach for scalable wireless power control"
    - "learning to optimize training deep neural networks for interference management"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep sets"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "attention is all you need"
    - "interpreting cnn knowledge via an explanatory graph"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "parameter re initialization through cyclical batch size schedules"):
  with members:
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "integrated model batch and domain parallelism in training neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "don t decay the learning rate increase the batch size"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-8 (induced by source paper "trust region based adversarial attack on neural networks"):
  with members:
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "regularizing deep networks using efficient layerwise adversarial training"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "universal adversarial perturbations"
    - "wide residual 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)