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: "data dependent sample complexity of deep neural networks via lipschitz augmentation"
  Abstract: Existing Rademacher complexity bounds for neural networks rely only on norm control of the weight matrices and depend exponentially on depth via a product of the matrix norms. Lower bounds show that this exponential dependence on depth is unavoidable when no additional properties of the training data are considered. We suspect that this conundrum comes from the fact that these bounds depend on the training data only through the margin. In practice, many data-dependent techniques such as Batchnorm improve the generalization performance. For feedforward neural nets as well as RNNs, we obtain tighter Rademacher complexity bounds by considering additional data-dependent properties of the network: the norms of the hidden layers of the network, and the norms of the Jacobians of each layer with respect to the previous layers. Our bounds scale polynomially in depth when these empirical quantities are small, as is usually the case in practice. To obtain these bounds, we develop general tools for augmenting a sequence of functions to make their composition Lipschitz and then covering the augmented functions. Inspired by our theory, we directly regularize the network's Jacobians during training and empirically demonstrate that this improves test performance.

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 "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"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information theoretic local minima characterization and regularization"
    - "the information complexity of learning tasks their structure and their distance"

Connected through hyperedge HE-3 (induced by source paper "norm based generalisation bounds for multi class convolutional neural networks"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "size free generalization bounds for convolutional neural networks"
    - "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"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "data dependent pac bayes priors via differential privacy"
    - "the singular values of convolutional layers"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "on the generalization of bayesian deep nets for multi class classification"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "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-6 (induced by source paper "optimal regularization can mitigate double descent"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "harmless interpolation of noisy data in regression"
    - "deep double descent where bigger models and more data hurt"
    - "minimizers of the empirical risk and risk monotonicity"
    - "exact expressions for double descent and implicit regularization via surrogate random design"

Connected through hyperedge HE-7 (induced by source paper "learning imbalanced datasets with label distribution aware margin loss"):
  with members:
    - "diversity in faces"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "overfitting of neural nets under class imbalance analysis and improvements for segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "regularized learning for domain adaptation under label shifts"

Connected through hyperedge HE-8 (induced by source paper "fantastic generalization measures and where to find them"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"

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)