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: "hessian based analysis of large batch training and robustness to adversaries"
  Abstract: Large batch size training of Neural Networks has been shown to incur accuracy loss when trained with the current methods. The exact underlying reasons for this are still not completely understood. Here, we study large batch size training through the lens of the Hessian operator and robust optimization. In particular, we perform a Hessian based study to analyze exactly how the landscape of the loss function changes when training with large batch size. We compute the true Hessian spectrum, without approximation, by back-propagating the second derivative. Extensive experiments on multiple networks show that saddle-points are not the cause for generalization gap of large batch size training, and the results consistently show that large batch converges to points with noticeably higher Hessian spectrum. Furthermore, we show that robust training allows one to favor flat areas, as points with large Hessian spectrum show poor robustness to adversarial perturbation. We further study this relationship, and provide empirical and theoretical proof that the inner loop for robust training is a saddle-free optimization problem \textit{almost everywhere}. We present detailed experiments with five different network architectures, including a residual network, tested on MNIST, CIFAR-10, and CIFAR-100 datasets. We have open sourced our method which can be accessed at [1].

Connected through hyperedge HE-1 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-2 (induced by source paper "the break even point on optimization trajectories of deep neural networks"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "lca loss change allocation for neural network training"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "the local elasticity of neural networks"

Connected through hyperedge HE-3 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-4 (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"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-5 (induced by source paper "q bert hessian based ultra low precision quantization of bert"):
  with members:
    - "are sixteen heads really better than one"
    - "a tensorized transformer for language modeling"
    - "lightweight and efficient neural natural language processing with quaternion networks"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-6 (induced by source paper "how does learning rate decay help modern neural networks"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on the convergence of adam and beyond"
    - "transfusion understanding transfer learning for medical imaging"
    - "do better imagenet models transfer better"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "understanding and robustifying differentiable architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autodispnet improving disparity estimation with automl"
    - "best practices for scientific research on neural architecture search"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-8 (induced by source paper "the generalization stability tradeoff in neural network pruning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "sparse networks from scratch faster training without losing performance"
    - "the state of sparsity in deep neural networks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "learning efficient convolutional networks through network slimming"

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.CV(Computer Vision and Pattern Recognition)