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: "traditional and heavy tailed self regularization in neural network models"
  Abstract: Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints. Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify \emph{5+1 Phases of Training}, corresponding to increasing amounts of \emph{Implicit Self-Regularization}. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a `size scale' separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of \emph{Heavy-Tailed Self-Regularization}, similar to the self-organization seen in the statistical physics of disordered systems. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.

Connected through hyperedge HE-1 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data"):
  with members:
    - "huggingface s transformers state of the art natural language processing"
    - "pytorch an imperative style high performance deep learning library"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "the singular values of convolutional layers"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"

Connected through hyperedge HE-4 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design"):
  with members:
    - "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "implicit regularization in deep matrix factorization"
    - "bayesian experimental design using regularized determinantal point processes"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "implicit regularization in over parameterized neural networks"

Connected through hyperedge HE-5 (induced by source paper "implicit regularization of normalization methods"):
  with members:
    - "over parameterization as a catalyst for better generalization of deep relu network"
    - "two models of double descent for weak features"
    - "a quantitative analysis of the effect of batch normalization on gradient descent"
    - "implicit regularization in deep matrix factorization"
    - "theoretical issues in deep networks approximation optimization and generalization"
    - "luck matters understanding training dynamics of deep relu networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theoretical analysis of auto rate tuning by batch normalization"

Connected through hyperedge HE-6 (induced by source paper "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "understanding generalization and optimization performance of deep cnns"

Connected through hyperedge HE-7 (induced by source paper "periodic spectral ergodicity a complexity measure for deep neural networks and neural architecture search"):
  with members:
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "hark side of deep learning from grad student descent to automated machine learning"
    - "pytorch an imperative style high performance deep learning library"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "persistency of excitation for robustness of neural networks"):
  with members:
    - "step size matters in deep learning"
    - "risk and parameter convergence of logistic regression"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "understanding deep learning requires rethinking generalization"
    - "adversarial examples in the physical world"
    - "intriguing properties of neural 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)