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: "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
  Abstract: In this paper, we analyze a number of architectural features of Deep Neural Networks (DNNs), using the so-called Neural Tangent Kernel (NTK). The NTK describes the training trajectory and generalization of DNNs in the infinite-width limit. #R##N#In this limit, we show that for (fully-connected) DNNs, as the depth grows, two regimes appear: "freeze" (also known as "order"), where the (scaled) NTK converges to a constant (slowing convergence), and "chaos", where it converges to a Kronecker delta (limiting generalization). #R##N#We show that when using the scaled ReLU as a nonlinearity, we naturally end up in the "freeze". We show that Batch Normalization (BN) avoids the freeze regime by reducing the importance of the constant mode in the NTK. A similar effect is obtained by normalizing the nonlinearity which moves the network to the chaotic regime. #R##N#We uncover the same "freeze" and "chaos" modes in Deep Deconvolutional Networks (DC-NNs). The "freeze" regime is characterized by checkerboard patterns in the image space in addition to the constant modes in input space. Finally, we introduce a new NTK-based parametrization to eliminate border artifacts and we propose a layer-dependent learning rate to improve the convergence of DC-NNs. #R##N#We illustrate our findings by training DCGANs using our setup. When trained in the "freeze" regime, we see that the generator collapses to a checkerboard mode. We also demonstrate numerically that the generator collapse can be avoided and that good quality samples can be obtained, by tuning the nonlinearity to reach the "chaos" regime (without using batch normalization).

Connected through hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "a mean field theory of batch normalization"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "self training with noisy student improves imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization"):
  with members:
    - "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"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "on exact computation with an infinitely wide neural net"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (induced by source paper "disentangling trainability and generalization in deep learning"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "a mean field theory of batch normalization"
    - "finite depth and width corrections to the neural tangent kernel"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "on exact computation with an infinitely wide neural net"
    - "transfusion understanding transfer learning for medical imaging"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "the asymptotic spectrum of the hessian of dnn throughout training"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on exact computation with an infinitely wide neural net"
    - "gradient descent happens in a tiny subspace"
    - "a convergence theory for deep learning via over parameterization"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-5 (induced by source paper "on lipschitz bounds of general convolutional neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "lipschitz properties for deep convolutional networks"
    - "improved training of wasserstein gans"
    - "universal adversarial perturbations"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deep convolutional neural networks based on semi discrete frames"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

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)