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: "exploring generalization in deep learning"
  Abstract: With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. We then investigate how well the measures explain different observed phenomena.

Connected through hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"

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"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "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 "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "large scale structure of neural network loss landscapes"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"

Connected through hyperedge HE-4 (induced by source paper "is the meta learning idea able to improve the generalization of deep neural networks on the standard supervised learning"):
  with members:
    - "alpha maml adaptive model agnostic meta learning"
    - "on first order meta learning algorithms"
    - "training behavior of deep neural network in frequency domain"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "how to train your maml"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "auto meta automated gradient based meta learner search"

Connected through hyperedge HE-5 (induced by source paper "improve sgd training via aligning min batches"):
  with members:
    - "convolutional networks with dense connectivity"
    - "cold case the lost mnist digits"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "deep learning for classical japanese literature"
    - "escaping saddles with stochastic gradients"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-6 (induced by source paper "the role of invariance in spectral complexity based generalization bounds"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "de randomized pac bayes margin bounds applications to non convex and non smooth predictors"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "algorithm dependent generalization bounds for overparameterized deep residual networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-8 (induced by source paper "think global act local relating dnn generalisation and node level snr"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "tightening mutual information based bounds on generalization error"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "on variational bounds of mutual information"
    - "entropy and mutual information in models of deep neural networks"
    - "putting an end to end to end gradient isolated learning of representations"
    - "chaining mutual information and tightening generalization bounds"
    - "theory iiib generalization in deep 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)