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: "a flow model of neural networks"
  Abstract: Based on a natural connection between ResNet and transport equation or its characteristic equation, we propose a continuous flow model for both ResNet and plain net. Through this continuous model, a ResNet can be explicitly constructed as a refinement of a plain net. The flow model provides an alternative perspective to understand phenomena in deep neural networks, such as why it is necessary and sufficient to use 2-layer blocks in ResNets, why deeper is better, and why ResNets are even deeper, and so on. It also opens a gate to bring in more tools from the huge area of differential equations.

Connected through hyperedge HE-1 (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"
    - "learning across scales a multiscale method for convolution 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-2 (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"
    - "learning across scales a multiscale method for convolution 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-3 (induced by source paper "monge amp ere flow for generative modeling"):
  with members:
    - "residual networks as geodesic flows of diffeomorphisms"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "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"
    - "deep learning approximation for stochastic control problems"
    - "made masked autoencoder for distribution estimation"

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)