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: "competitive multi scale convolution"
  Abstract: In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters. This new module is inspired by the inception module, where we replace the original collaborative pooling stage (consisting of a concatenation of the multi-scale filter outputs) by a competitive pooling represented by a maxout activation unit. This extension has the following two objectives: 1) the selection of the maximum response among the multi-scale filters prevents filter co-adaptation and allows the formation of multiple sub-networks within the same model, which has been shown to facilitate the training of complex learning problems; and 2) the maxout unit reduces the dimensionality of the outputs from the multi-scale filters. We show that the use of our proposed module in typical deep ConvNets produces classification results that are either better than or comparable to the state of the art on the following benchmark datasets: MNIST, CIFAR-10, CIFAR-100 and SVHN.

Connected through hyperedge HE-1 (induced by source paper "towards learning affine invariant representations via data efficient cnns"):
  with members:
    - "gabor convolutional networks"
    - "differentiable mpc for end to end planning and control"
    - "dynamic filtering with large sampling field for convnets"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "targeted kernel networks faster convolutions with attentive regularization"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "interpretable convolutional neural networks"
    - "attentional pooling for action recognition"
    - "more is less a more complicated network with less inference complexity"
    - "attentive recurrent comparators"

Connected through hyperedge HE-3 (induced by source paper "multi scale convolution aggregation and stochastic feature reuse for densenets"):
  with members:
    - "group normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "deep layer aggregation"
    - "dilated residual networks"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural network design patterns"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "weighted residuals for very deep networks"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "convolutional neural fabrics"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "transitioning between convolutional and fully connected layers in neural networks"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "visualizing and understanding convolutional networks"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "fractalnet ultra deep neural networks without residuals"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "wide residual networks"
    - "all you need is a good init"

Connected through hyperedge HE-7 (induced by source paper "deeply fused nets"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "all you need is a good init"
    - "holistically nested edge detection"
    - "batch normalized maxout network in network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "net2net accelerating learning via knowledge transfer"
    - "path sgd path normalized optimization in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "semantic segmentation of earth observation data using multimodal and multi scale deep networks"):
  with members:
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)