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: "towards understanding the role of over parametrization in generalization of neural networks"
  Abstract: Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization. In this work we suggest a novel complexity measure based on unit-wise capacities resulting in a tighter generalization bound for two layer ReLU networks. Our capacity bound correlates with the behavior of test error with increasing network sizes, and could potentially explain the improvement in generalization with over-parametrization. We further present a matching lower bound for the Rademacher complexity that improves over previous capacity lower bounds for neural 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"
    - "kernel and deep regimes in overparametrized models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "the singular values of convolutional layers"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "tensor contraction layers for parsimonious deep nets"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-3 (induced by source paper "identifying critical neurons in ann architectures using mixed integer programming"):
  with members:
    - "lossless compression of deep neural networks"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "pruning at a glance global neural pruning for model compression"
    - "a benchmark for interpretability methods in deep neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "understanding individual neuron importance using information theory"
    - "pruning deep neural networks using partial least squares"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "stochasticity of deterministic gradient descent large learning rate for multiscale objective function"):
  with members:
    - "variational optimization on lie groups with examples of leading generalized eigenvalue problems"
    - "analysis of momentum methods"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-5 (induced by source paper "the early phase of neural network training"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "stabilizing the lottery ticket hypothesis"
    - "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"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "unsupervised representation learning by predicting image rotations"
    - "gradient descent happens in a tiny subspace"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-6 (induced by source paper "on the generalization of bayesian deep nets for multi class classification"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "data dependent stability of stochastic gradient descent"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"

Connected through hyperedge HE-7 (induced by source paper "al2 progressive activation loss for learning general representations in classification neural networks"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "do imagenet classifiers generalize to imagenet"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "dimensionality driven learning with noisy labels"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "exploring generalization in deep learning"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-8 (induced by source paper "coherent gradients an approach to understanding generalization in gradient descent based optimization"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "circuit based intrinsic methods to detect overfitting"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "deep learning is robust to massive label noise"
    - "the global optimization geometry of shallow linear neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

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)