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 generalization of deep learning perspective of loss landscapes"
  Abstract: It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don't. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.

Connected through hyperedge HE-1 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "where is the information in a deep neural network"
    - "asymmetric valleys beyond sharp and flat local minima"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "don t decay the learning rate increase the batch size"
    - "sharp minima can generalize for deep nets"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-2 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-3 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "a mean field theory of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "fixup initialization residual learning without normalization"
    - "stiffness a new perspective on generalization in neural networks"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-4 (induced by source paper "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "network compression using correlation analysis of layer responses"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "information theoretic local minima characterization and regularization"):
  with members:
    - "lightlike neuromanifolds occam s razor and deep learning"
    - "gradient descent finds global minima of deep neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "averaging weights leads to wider optima and better generalization"
    - "skip connections eliminate singularities"
    - "optimization landscape and expressivity of deep cnns"
    - "exploring generalization in deep learning"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-6 (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 self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "asymmetric valleys beyond sharp and flat local minima"):
  with members:
    - "on the local minima of the empirical risk"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the loss landscape of overparameterized neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "averaging weights leads to wider optima and better generalization"
    - "risk and parameter convergence of logistic regression"
    - "revisiting small batch training for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "luck matters understanding training dynamics of deep relu networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning is robust to massive label noise"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a theoretical framework for deep and locally connected relu network"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)