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: "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
  Abstract: The paper characterizes classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.

Connected through hyperedge HE-1 (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"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"

Connected through hyperedge HE-2 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "genesis of basic and multi layer echo state network recurrent autoencoders for efficient data representations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "transfer learning for time series classification"

Connected through hyperedge HE-3 (induced by source paper "information bottleneck and its applications in deep learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"
    - "mathematics of deep learning"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-4 (induced by source paper "doctor of crosswise reducing over parametrization in neural networks"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "mckernel approximate kernel expansions in log linear time through randomization"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"):
  with members:
    - "generating long sequences with sparse transformers"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-6 (induced by source paper "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "extracting structured dynamical systems using sparse optimization with very few samples"
    - "stable architectures for deep neural networks"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"

Connected through hyperedge HE-7 (induced by source paper "optimal function approximation with relu neural networks"):
  with members:
    - "universality of deep convolutional neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "expressive power of recurrent neural networks"
    - "the power of deeper networks for expressing natural functions"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"

Connected through hyperedge HE-8 (induced by source paper "deepxde a deep learning library for solving differential equations"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "locally adaptive activation functions with slope recovery term for deep and physics informed neural networks"
    - "deeponet learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators"
    - "a review of automatic differentiation and its efficient implementation"
    - "learning in modal space solving time dependent stochastic pdes using physics informed neural networks"
    - "physics informed neural networks for multiphysics data assimilation with application to subsurface transport"
    - "a deep neural network surrogate for high dimensional random partial differential equations"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"

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)