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: "failures of gradient based deep learning"
  Abstract: In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four types of simple problems, for which the gradient-based algorithms commonly used in deep learning either fail or suffer from significant difficulties. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.

Connected through hyperedge HE-1 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "improving sequence to sequence learning via optimal transport"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "dropblock a regularization method for convolutional networks"
    - "policy optimization as wasserstein gradient flows"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-2 (induced by source paper "introduction to camera pose estimation with deep learning"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "learning loss for active learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "deep auxiliary learning for visual localization and odometry"

Connected through hyperedge HE-3 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-4 (induced by source paper "explicitizing an implicit bias of the frequency principle in two layer neural networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "a type of generalization error induced by initialization in deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-5 (induced by source paper "feature generation by convolutional neural network for click through rate prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "learning to generate reviews and discovering sentiment"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-6 (induced by source paper "frequency principle fourier analysis sheds light on deep neural networks"):
  with members:
    - "theory of the frequency principle for general deep neural networks"
    - "training behavior of deep neural network in frequency domain"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "frequency principle in deep learning with general loss functions and its potential application"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "towards understanding generalization of deep learning perspective of loss landscapes"

Connected through hyperedge HE-7 (induced by source paper "closing the gap towards end to end autonomous vehicle system"):
  with members:
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "deep density networks and uncertainty in recommender systems"
    - "end to end multi modal multi task vehicle control for self driving cars with visual perception"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "focal loss for dense object detection"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end driving via conditional imitation learning"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "id3 learns juntas for smoothed product distributions"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "beyond the low degree algorithm mixtures of subcubes and their applications"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized 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)