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 kernels in convolutional neural networks for image classification"
  Abstract: Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define Receptive Fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we first propose a feature visualization method for visualization of pixel-wise classification score maps of learned features. Motivated by our experimental results, and observations reported in the literature for modeling of visual systems, we propose a novel design of shape of kernels for learning of representations in CNNs. In the experimental results, we achieved a state-of-the-art classification performance compared to a base CNN model [28] by reducing the number of parameters and computational time of the model using the ILSVRC-2012 dataset [24]. The proposed models also outperform the state-of-the-art models employed on the CIFAR-10/100 datasets [12] for image classification. Additionally, we analyzed the robustness of the proposed method to occlusion for classification of partially occluded images compared with the state-of-the-art methods. Our results indicate the effectiveness of the proposed approach. The code is available in github.com/minogame/caffe-qhconv.

Connected through hyperedge HE-1 (induced by source paper "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"):
  with members:
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "learning efficient convolutional networks through network slimming"
    - "learning transferable architectures for scalable image recognition"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "orientation aware semantic segmentation on icosahedron spheres"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "cubenet equivariance to 3d rotation and translation"
    - "kernel transformer networks for compact spherical convolution"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"

Connected through hyperedge HE-3 (induced by source paper "state classification with cnn"):
  with members:
    - "functional object oriented network for manipulation learning"
    - "densely connected convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "flattened convolutional neural networks for feedforward acceleration"
    - "fast image scanning with deep max pooling convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "irregular convolutional neural networks"):
  with members:
    - "deformable convolutional networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "multiple vlad encoding of cnns for image classification"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "dense image representation with spatial pyramid vlad coding of cnn for locally robust captioning"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "fast r cnn"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)