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: "fantastic generalization measures and where to find them"
  Abstract: Generalization of deep networks has been of great interest in recent years, resulting in a number of theoretically and empirically motivated complexity measures. However, most papers proposing such measures study only a small set of models, leaving open the question of whether the conclusion drawn from those experiments would remain valid in other settings. We present the first large scale study of generalization in deep networks. We investigate more then 40 complexity measures taken from both theoretical bounds and empirical studies. We train over 10,000 convolutional networks by systematically varying commonly used hyperparameters. Hoping to uncover potentially causal relationships between each measure and generalization, we analyze carefully controlled experiments and show surprising failures of some measures as well as promising measures for further research.

Connected through hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "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"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-2 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited"):
  with members:
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "deep double descent where bigger models and more data hurt"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "functional variational bayesian neural networks"
    - "detecting extrapolation with local ensembles"
    - "semi flat minima and saddle points by embedding neural networks to overparameterization"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-3 (induced by source paper "generalization bounds for deep convolutional neural networks"):
  with members:
    - "norm based generalisation bounds for multi class convolutional neural networks"
    - "generalization in deep networks the role of distance from initialization"
    - "generalization bounds for neural networks via approximate description length"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "the singular values of convolutional layers"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural 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)