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: "learning across scales a multiscale method for convolution neural networks"
  Abstract: In this work we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted as a time-dependent nonlinear differential equation and learning as controlling the parameters of the differential equation such that the network approximates the data-label relation for given training data. Using this continuous interpretation we derive two new methods to scale CNNs with respect to two different dimensions. The first class of multiscale methods connects low-resolution and high-resolution data through prolongation and restriction of CNN parameters. We demonstrate that this enables classifying high-resolution images using CNNs trained with low-resolution images and vice versa and warm-starting the learning process. The second class of multiscale methods connects shallow and deep networks and leads to new training strategies that gradually increase the depths of the CNN while re-using parameters for initializations.

Connected through hyperedge HE-1 (induced by source paper "a derivative free method for solving elliptic partial differential equations with deep neural networks"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "highway and residual networks learn unrolled iterative estimation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "mgnet a unified framework of multigrid and convolutional neural network"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "learning neural pde solvers with convergence guarantees"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "universality of deep convolutional neural networks"
    - "a flow model of neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "dual path networks"

Connected through hyperedge HE-3 (induced by source paper "constrained linear data feature mapping for image classification"):
  with members:
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "mgnet a unified framework of multigrid and convolutional neural network"
    - "learning neural pde solvers with convergence guarantees"
    - "optimization algorithm inspired deep neural network structure design"
    - "a flow model of neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural networks combined with runge kutta methods"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "deep learning with data dependent implicit activation function"
    - "neural ordinary differential equations"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "memory efficient implementation of densenets"
    - "maximum principle based algorithms for deep learning"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "reversible architectures for arbitrarily deep residual neural networks"

Connected through hyperedge HE-5 (induced by source paper "stochastic training of residual networks a differential equation viewpoint"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "laplacian smoothing gradient descent"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "deep residual learning and pdes on manifold"
    - "don t decay the learning rate increase the batch size"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-6 (induced by source paper "deep neural networks motivated by partial differential equations"):
  with members:
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "the reversible residual network backpropagation without storing activations"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "maximum principle based algorithms for deep learning"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "reversible architectures for arbitrarily deep residual neural networks"

Connected through hyperedge HE-7 (induced by source paper "low cost parameterizations of deep convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "stable tensor neural networks for rapid deep learning"):
  with members:
    - "stable architectures for deep neural networks"
    - "failures of gradient based deep learning"
    - "deep vs shallow networks an approximation theory perspective"
    - "tensorizing neural networks"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "deep residual learning for image recognition"
    - "the limitations of deep learning in adversarial settings"
    - "predicting parameters in deep learning"

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.NE(Neural and Evolutionary Computing)