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: "deep convolutional neural networks with merge and run mappings"
  Abstract: A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow. To further reduce the training difficulty, we present a simple network architecture, deep merge-and-run neural networks. The novelty lies in a modularized building block, merge-and-run block, which assembles residual branches in parallel through a merge-and-run mapping: Average the inputs of these residual branches (Merge), and add the average to the output of each residual branch as the input of the subsequent residual branch (Run), respectively. We show that the merge-and-run mapping is a linear idempotent function in which the transformation matrix is idempotent, and thus improves information flow, making training easy. In comparison to residual networks, our networks enjoy compelling advantages: they contain much shorter paths, and the width, i.e., the number of channels, is increased. We evaluate the performance on the standard recognition tasks. Our approach demonstrates consistent improvements over ResNets with the comparable setup, and achieves competitive results (e.g., $3.57\%$ testing error on CIFAR-$10$, $19.00\%$ on CIFAR-$100$, $1.51\%$ on SVHN).

Connected through hyperedge HE-1 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "accelerating deep neural networks with spatial bottleneck modules"):
  with members:
    - "squeeze and excitation networks"
    - "dual path networks"
    - "channel pruning for accelerating very deep neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deeply fused nets"

Connected through hyperedge HE-3 (induced by source paper "igcv3 interleaved low rank group convolutions for efficient deep neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "on the expressive power of overlapping architectures of deep learning"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "channel splitting network for single mr image super resolution"):
  with members:
    - "single image super resolution via cascaded multi scale cross network"
    - "brain mri super resolution using 3d deep densely connected neural networks"
    - "residual dense network for image super resolution"
    - "aerial spectral super resolution using conditional adversarial networks"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "dual path networks"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (induced by source paper "waveletnet logarithmic scale efficient convolutional neural networks for edge devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "interleaved group convolutions for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "optimization algorithm inspired deep neural network structure design"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "recurrent inference machines for solving inverse problems"
    - "practical block wise neural network architecture generation"
    - "convolutional neural fabrics"
    - "maximal sparsity with deep networks"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "mixed link networks"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "deeply fused nets"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "condensenet an efficient densenet using learned group convolutions"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "learning transferable architectures for scalable image recognition"
    - "interleaved group convolutions for deep neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)