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: "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
  Abstract: Random Matrix Theory (RMT) is applied to analyze 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 DNN training process itself implicitly implements a form of Self-Regularization. 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 explicit regularization. Building on relatively recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. These phases can be observed during the training process as well as in the final learned DNNs. 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 Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This results from correlations arising at all size scales, which arises implicitly due to the training process itself. 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. This demonstrates that---all else being equal---DNN optimization with larger batch sizes leads to less-well implicitly-regularized models, and it provides an explanation for the generalization gap phenomena.

Connected through hyperedge HE-1 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "orthogonal deep neural networks"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "interpretable convolutional neural networks via feedforward design"
    - "regularization for deep learning a taxonomy"

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"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "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 "balancedness and alignment are unlikely in linear neural networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "memorization in overparameterized autoencoders"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "implicit regularization in deep matrix factorization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-4 (induced by source paper "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"):
  with members:
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "hark side of deep learning from grad student descent to automated machine learning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "do imagenet classifiers generalize to imagenet"
    - "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
    - "regularized evolution for image classifier architecture search"
    - "universal approximations of invariant maps by neural networks"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "universality of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "on dropout and nuclear norm regularization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the implicit bias of dropout"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a comprehensive analysis of deep regression"
    - "deep linear neural networks with arbitrary loss all local minima are global"

Connected through hyperedge HE-7 (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"
    - "traditional and heavy tailed self regularization in neural network models"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "implicit regularization of normalization methods"):
  with members:
    - "over parameterization as a catalyst for better generalization of deep relu network"
    - "traditional and heavy tailed self regularization in neural network models"
    - "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"
    - "theoretical analysis of auto rate tuning by batch normalization"

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)