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: "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
  Abstract: Given two or more Deep Neural Networks (DNNs) with the same or similar architectures, and trained on the same dataset, but trained with different solvers, parameters, hyper-parameters, regularization, etc., can we predict which DNN will have the best test accuracy, and can we do so without peeking at the test data? In this paper, we show how to use a new Theory of Heavy-Tailed Self-Regularization (HT-SR) to answer this. HT-SR suggests, among other things, that modern DNNs exhibit what we call Heavy-Tailed Mechanistic Universality (HT-MU), meaning that the correlations in the layer weight matrices can be fit to a power law with exponents that lie in common Universality classes from Heavy-Tailed Random Matrix Theory (HT-RMT). From this, we develop a Universal capacity control metric that is a weighted average of these PL exponents. Rather than considering small toy NNs, we examine over 50 different, large-scale pre-trained DNNs, ranging over 15 different architectures, trained on ImagetNet, each of which has been reported to have different test accuracies. We show that this new capacity metric correlates very well with the reported test accuracies of these DNNs, looking across each architecture (VGG16/.../VGG19, ResNet10/.../ResNet152, etc.). We also show how to approximate the metric by the more familiar Product Norm capacity measure, as the average of the log Frobenius norm of the layer weight matrices. Our approach requires no changes to the underlying DNN or its loss function, it does not require us to train a model (although it could be used to monitor training), and it does not even require access to the ImageNet data.

Connected through hyperedge HE-1 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "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:
    - "traditional and heavy tailed self regularization in neural network models"
    - "huggingface s transformers state of the art natural language processing"
    - "pytorch an imperative style high performance deep learning library"
    - "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 "periodic spectral ergodicity a complexity measure for deep neural networks and neural architecture search"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "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"
    - "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-4 (induced by source paper "traditional and heavy tailed self regularization in neural network models"):
  with members:
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "regularization for deep learning a taxonomy"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-5 (induced by source paper "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "deep learning is robust to massive label noise"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"
    - "a capacity scaling law for artificial 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)