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: "uniform convergence may be unable to explain generalization in deep learning"
  Abstract: Aimed at explaining the surprisingly good generalization behavior of overparameterized deep networks, recent works have developed a variety of generalization bounds for deep learning, all based on the fundamental learning-theoretic technique of uniform convergence. While it is well-known that many of these existing bounds are numerically large, through numerous experiments, we bring to light a more concerning aspect of these bounds: in practice, these bounds can {\em increase} with the training dataset size. Guided by our observations, we then present examples of overparameterized linear classifiers and neural networks trained by gradient descent (GD) where uniform convergence provably cannot ``explain generalization'' -- even if we take into account the implicit bias of GD {\em to the fullest extent possible}. More precisely, even if we consider only the set of classifiers output by GD, which have test errors less than some small $\epsilon$ in our settings, we show that applying (two-sided) uniform convergence on this set of classifiers will yield only a vacuous generalization guarantee larger than $1-\epsilon$. Through these findings, we cast doubt on the power of uniform convergence-based generalization bounds to provide a complete picture of why overparameterized deep networks generalize well.

Connected through hyperedge HE-1 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation"):
  with members:
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "limitations of the empirical fisher approximation"
    - "information theoretic local minima characterization and regularization"
    - "the information complexity of learning tasks their structure and their distance"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-2 (induced by source paper "the role of invariance in spectral complexity based generalization bounds"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "information matrices and generalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding generalization and optimization performance of deep cnns"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-3 (induced by source paper "topologically densified distributions"):
  with members:
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "a kernel theory of modern data augmentation"
    - "utilizing class information for deep network representation shaping"
    - "augment your batch better training with larger batches"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a topological regularizer for classifiers via persistent homology"

Connected through hyperedge HE-4 (induced by source paper "hold me tight influence of discriminative features on deep network boundaries"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "a fourier perspective on model robustness in computer vision"
    - "pytorch an imperative style high performance deep learning library"
    - "robustness via curvature regularization and vice versa"
    - "adversarial spheres"
    - "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"
    - "on the decision boundary of deep neural networks"
    - "with friends like these who needs adversaries"

Connected through hyperedge HE-5 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack"):
  with members:
    - "explaining landscape connectivity of low cost solutions for multilayer nets"
    - "barron spaces and the compositional function spaces for neural network models"
    - "a priori estimates of the population risk for residual networks"
    - "deep sets"
    - "rethinking the value of network pruning"
    - "expressive power of recurrent neural networks"
    - "error bounds for approximations with deep relu networks"
    - "learning functions when is deep better than shallow"

Connected through hyperedge HE-6 (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"
    - "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"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-7 (induced by source paper "can implicit bias explain generalization stochastic convex optimization as a case study"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "understanding deep learning requires rethinking generalization"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-8 (induced by source paper "linear mode connectivity and the lottery ticket hypothesis"):
  with members:
    - "the early phase of neural network training"
    - "picking winning tickets before training by preserving gradient flow"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "an exponential learning rate schedule for deep learning"
    - "what s hidden in a randomly weighted neural network"
    - "the state of sparsity in deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable 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)