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: "reversible architectures for arbitrarily deep residual neural networks"
  Abstract: Recently, deep residual networks have been successfully applied in many computer vision and natural language processing tasks, pushing the state-of-the-art performance with deeper and wider architectures. In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success. From this interpretation, we develop a theoretical framework on stability and reversibility of deep neural networks, and derive three reversible neural network architectures that can go arbitrarily deep in theory. The reversibility property allows a memory-efficient implementation, which does not need to store the activations for most hidden layers. Together with the stability of our architectures, this enables training deeper networks using only modest computational resources. We provide both theoretical analyses and empirical results. Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance. Furthermore, we show our architectures yield superior results when trained using fewer training data.

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 "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"
    - "stable architectures for deep neural networks"
    - "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"

Connected through hyperedge HE-3 (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"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"

Connected through hyperedge HE-4 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization"):
  with members:
    - "symmetric block low rank layers for fully reversible multilevel neural networks"
    - "imexnet a forward stable deep neural network"
    - "fully hyperbolic convolutional neural networks"
    - "anodev2 a coupled neural ode evolution framework"
    - "leanconvnets low cost yet effective convolutional neural networks"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "leanconvnets low cost yet effective convolutional neural networks"):
  with members:
    - "imexnet a forward stable deep neural network"
    - "deep learning for audio signal processing"
    - "mixconv mixed depthwise convolutional kernels"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"

Connected through hyperedge HE-6 (induced by source paper "reversible gans for memory efficient image to image translation"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "lifting layers analysis and applications"
    - "neural ordinary differential equations"
    - "i revnet deep invertible networks"
    - "deeper image quality transfer training low memory neural networks for 3d images"
    - "the reversible residual network backpropagation without storing activations"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "leanresnet a low cost yet effective convolutional residual networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "dynamical system inspired adaptive time stepping controller for residual network families"):
  with members:
    - "towards robust resnet a small step but a giant leap"
    - "imexnet a forward stable deep neural network"
    - "smooth inter layer propagation of stabilized neural networks for classification"
    - "convolutional neural networks with alternately updated clique"
    - "spectral normalization for generative adversarial networks"
    - "invertible residual networks"
    - "optimization algorithm inspired deep neural network structure design"
    - "stable architectures for deep neural networks"

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.CV(Computer Vision and Pattern Recognition)