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: "design of efficient convolutional layers using single intra channel convolution topological subdivisioning and spatial bottleneck structure"
  Abstract: Deep convolutional neural networks achieve remarkable visual recognition performance, at the cost of high computational complexity. In this paper, we have a new design of efficient convolutional layers based on three schemes. The 3D convolution operation in a convolutional layer can be considered as performing spatial convolution in each channel and linear projection across channels simultaneously. By unravelling them and arranging the spatial convolution sequentially, the proposed layer is composed of a single intra-channel convolution, of which the computation is negligible, and a linear channel projection. A topological subdivisioning is adopted to reduce the connection between the input channels and output channels. Additionally, we also introduce a spatial "bottleneck" structure that utilizes a convolution-projection-deconvolution pipeline to take advantage of the correlation between adjacent pixels in the input. Our experiments demonstrate that the proposed layers remarkably outperform the standard convolutional layers with regard to accuracy/complexity ratio. Our models achieve similar accuracy to VGG, ResNet-50, ResNet-101 while requiring 42, 4.5, 6.5 times less computation respectively.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "butterfly net2 simplified butterfly net and fourier transform initialization"):
  with members:
    - "neumann networks for inverse problems in imaging"
    - "benchmarking denoising algorithms with real photographs"
    - "image to image translation with conditional adversarial networks"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "flattened convolutional neural networks for feedforward acceleration"

Connected through hyperedge HE-4 (induced by source paper "searching for efficient multi scale architectures for dense image prediction"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-5 (induced by source paper "encoder decoder with atrous separable convolution for semantic image segmentation"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"

Connected through hyperedge HE-6 (induced by source paper "improving efficiency in convolutional neural networks with multilinear filters"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor contraction layers for parsimonious deep nets"
    - "tensor representation in high frequency financial data for price change prediction"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "mobilenetv2 inverted residuals and linear bottlenecks"):
  with members:
    - "connectivity learning in multi branch networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "learning time efficient deep architectures with budgeted super networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the power of sparsity in convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "penetrating the fog the path to efficient cnn models"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "mixed precision training"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

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)