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: "convolutional neural networks applied to house numbers digit classification"
  Abstract: We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.

Connected through hyperedge HE-1 (induced by source paper "convolutional networks with dense connectivity"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-2 (induced by source paper "a system for massively parallel hyperparameter tuning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "a generalized framework for population based training"
    - "darts differentiable architecture search"
    - "tune a research platform for distributed model selection and training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "regularizing and optimizing lstm language models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-3 (induced by source paper "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network"):
  with members:
    - "building efficient cnn architecture for offline handwritten chinese character recognition"
    - "generative adversarial network for medical images mi gan"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"

Connected through hyperedge HE-4 (induced by source paper "deepsquare boosting the learning power of deep convolutional neural networks with elementwise square operators"):
  with members:
    - "global second order pooling convolutional networks"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep anchored convolutional neural networks"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "learning multiple visual domains with residual adapters"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "sharesnet reducing residual network parameter number by sharing weights"
    - "deep networks with stochastic depth"
    - "eie efficient inference engine on compressed deep neural network"
    - "wide residual networks"

Connected through hyperedge HE-7 (induced by source paper "local context normalization revisiting local normalization"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "learning multiple visual domains with residual adapters"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance normalization the missing ingredient for fast stylization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "denser deep evolutionary network structured representation"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "towards the evolution of multi layered neural networks a dynamic structured grammatical evolution approach"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "pruning convolutional neural networks for resource efficient inference"
    - "cma es for hyperparameter optimization of 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)