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: "the singular values of convolutional layers"
  Abstract: We characterize the singular values of the linear transformation associated with a convolution applied to a two-dimensional feature map with multiple channels. Our characterization enables efficient computation of the singular values of convolutional layers used in popular deep neural network architectures. It also leads to an algorithm for projecting a convolutional layer onto the set of layers obeying a bound on the operator norm of the layer. We show that this is an effective regularizer; periodically applying these projections during training improves the test error of a residual network on CIFAR-10 from 6.2\% to 5.3\%.

Connected through hyperedge HE-1 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "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"
    - "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-2 (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"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "data dependent pac bayes priors via differential privacy"

Connected through hyperedge HE-3 (induced by source paper "lookahead a far sighted alternative of magnitude based pruning"):
  with members:
    - "backpack packing more into backprop"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "the state of sparsity in deep neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-4 (induced by source paper "gradient ell_1 regularization for quantization robustness"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "defensive quantization when efficiency meets robustness"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "sparse dnns with improved adversarial robustness"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-5 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "huggingface s transformers state of the art natural language processing"
    - "pytorch an imperative style high performance deep learning library"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (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-7 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "truth or backpropaganda an empirical investigation of deep learning theory"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "fixup initialization residual learning without normalization"
    - "understanding generalization through visualizations"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "gradient descent aligns the layers of deep linear 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)