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: "stable architectures for deep neural networks"
  Abstract: Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

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"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-2 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-4 (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"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "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-5 (induced by source paper "dissecting neural odes"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "imexnet a forward stable deep neural network"
    - "anodev2 a coupled neural ode evolution framework"
    - "deep learning via dynamical systems an approximation perspective"
    - "state space representations of deep neural networks"
    - "neural jump stochastic differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "deep equilibrium models"

Connected through hyperedge HE-6 (induced by source paper "pde based group equivariant convolutional neural networks"):
  with members:
    - "kercnns biologically inspired lateral connections for classification of corrupted images"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "a cortical inspired model for orientation dependent contrast perception a link with wilson cowan equations"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "learning steerable filters for rotation equivariant cnns"
    - "learning so 3 equivariant representations with spherical cnns"

Connected through hyperedge HE-7 (induced by source paper "a non intrusive correction algorithm for classification problems with corrupted data"):
  with members:
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "certified defenses for data poisoning attacks"
    - "learning from noisy singly labeled data"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "scalable gradients for stochastic differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "latent odes for irregularly sampled time series"
    - "neural jump stochastic differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "deep learning with differential gaussian process flows"
    - "deep neural networks motivated by partial differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

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)