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: "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
  Abstract: It is well known that the initialization of weights in deep neural networks can have a dramatic impact on learning speed. For example, ensuring the mean squared singular value of a network's input-output Jacobian is $O(1)$ is essential for avoiding the exponential vanishing or explosion of gradients. The stronger condition that all singular values of the Jacobian concentrate near $1$ is a property known as dynamical isometry. For deep linear networks, dynamical isometry can be achieved through orthogonal weight initialization and has been shown to dramatically speed up learning; however, it has remained unclear how to extend these results to the nonlinear setting. We address this question by employing powerful tools from free probability theory to compute analytically the entire singular value distribution of a deep network's input-output Jacobian. We explore the dependence of the singular value distribution on the depth of the network, the weight initialization, and the choice of nonlinearity. Intriguingly, we find that ReLU networks are incapable of dynamical isometry. On the other hand, sigmoidal networks can achieve isometry, but only with orthogonal weight initialization. Moreover, we demonstrate empirically that deep nonlinear networks achieving dynamical isometry learn orders of magnitude faster than networks that do not. Indeed, we show that properly-initialized deep sigmoidal networks consistently outperform deep ReLU networks. Overall, our analysis reveals that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks"):
  with members:
    - "a mean field theory of batch normalization"
    - "fixup initialization residual learning without normalization"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "weight standardization"
    - "a quantization friendly separable convolution for mobilenets"
    - "group normalization"
    - "spectrum concentration in deep residual learning a free probability approach"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the distance between two neural networks and the stability of learning"):
  with members:
    - "optimization for deep learning theory and algorithms"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "huggingface s transformers state of the art natural language processing"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-3 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "stable architectures for deep neural networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "depth creates no bad local minima"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "rezero is all you need fast convergence at large depth"):
  with members:
    - "batch normalization biases deep residual networks towards shallow paths"
    - "on layer normalization in the transformer architecture"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "self normalizing neural networks"
    - "mean field residual networks on the edge of chaos"

Connected through hyperedge HE-5 (induced by source paper "convolutional spectral kernel learning"):
  with members:
    - "automated spectral kernel learning"
    - "orthogonal deep neural networks"
    - "functional variational bayesian neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "differentiable compositional kernel learning for gaussian processes"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-6 (induced by source paper "residual tangent kernels"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "finite depth and width corrections to the neural tangent kernel"
    - "on exact computation with an infinitely wide neural net"
    - "mean field residual networks on the edge of chaos"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"):
  with members:
    - "a mean field theory of batch normalization"
    - "entropy and mutual information in models of deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "exploration by random network distillation"
    - "fisher information and natural gradient learning of random deep networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-8 (induced by source paper "orthogonal deep neural networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the singular values of convolutional layers"
    - "on the effect of pooling on the geometry of representations"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"
    - "global optimality conditions for deep neural networks"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

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)