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: "kernel and deep regimes in overparametrized models"
  Abstract: A recent line of work studies overparametrized neural networks in the "kernel regime," i.e. when the network behaves during training as a kernelized linear predictor, and thus training with gradient descent has the effect of finding the minimum RKHS norm solution. This stands in contrast to other studies which demonstrate how gradient descent on overparametrized multilayer networks can induce rich implicit biases that are not RKHS norms. Building on an observation by Chizat and Bach, we show how the scale of the initialization controls the transition between the "kernel" (aka lazy) and "rich" (aka active) regimes and affects generalization properties in multilayer homogeneous models. We also highlight an interesting role for the width of a model in the case that the predictor is not identically zero at initialization. We provide a complete and detailed analysis for a family of simple depth-$D$ models that already exhibit an interesting and meaningful transition between the kernel and rich regimes, and we also demonstrate this transition empirically for more complex matrix factorization models and multilayer non-linear networks.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "deep double descent where bigger models and more data hurt"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (induced by source paper "frequency bias in neural networks for input of non uniform density"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "towards understanding the spectral bias of deep learning"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the convergence rate of training recurrent neural networks"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "eigenvalue decay implies polynomial time learnability for neural networks"

Connected through hyperedge HE-4 (induced by source paper "beyond linearization on quadratic and higher order approximation of wide neural networks"):
  with members:
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-5 (induced by source paper "removable and or repeated units emerge in overparametrized deep neural networks"):
  with members:
    - "intrinsic dimension of data representations in deep neural networks"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "thinet a filter level pruning method for deep neural network compression"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-6 (induced by source paper "harmless interpolation of noisy data in regression"):
  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"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-7 (induced by source paper "on learning over parameterized neural networks a functional approximation prospective"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural spectrum alignment"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "asymptotics of wide networks from feynman diagrams"
    - "gradient dynamics of shallow univariate relu networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "general probabilistic surface optimization and log density estimation"
    - "neural tangent kernel convergence and generalization in 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)