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: "a modern take on the bias variance tradeoff in neural networks"
  Abstract: The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve. However, recent empirical results with over-parameterized neural networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks. This suggests that there might not be a bias-variance tradeoff in neural networks with respect to network width, unlike was originally claimed by, e.g., Geman et al. (1992). Motivated by the shaky evidence used to support this claim in neural networks, we measure bias and variance in the modern setting. We find that both bias and variance can decrease as the number of parameters grows. To better understand this, we introduce a new decomposition of the variance to disentangle the effects of optimization and data sampling. We also provide theoretical analysis in a simplified setting that is consistent with our empirical findings.

Connected through hyperedge HE-1 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-2 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "kernel and deep regimes in overparametrized models"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "deep double descent where bigger models and more data hurt"
    - "on exact computation with an infinitely wide neural net"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "rethinking bias variance trade off for generalization of neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"

Connected through hyperedge HE-6 (induced by source paper "exploring the limitations of behavior cloning for autonomous driving"):
  with members:
    - "causal confusion in imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "rethinking self driving multi task knowledge for better generalization and accident explanation ability"
    - "driving policy transfer via modularity and abstraction"
    - "on offline evaluation of vision based driving models"
    - "end to end learning of driving models with surround view cameras and route planners"

Connected through hyperedge HE-7 (induced by source paper "finding the needle in the haystack with convolutions on the benefits of architectural bias"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "shaping the learning landscape in neural networks around wide flat minima"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "three factors influencing minima in sgd"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

Connected through hyperedge HE-8 (induced by source paper "disentangling feature and lazy learning in deep neural networks an empirical study"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "asymptotics of wide networks from feynman diagrams"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "the asymptotic spectrum of the hessian of dnn throughout training"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"

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)