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 epitomic convolutional neural networks"
  Abstract: Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new building block for deep neural networks. An epitomic convolution layer replaces a pair of consecutive convolution and max-pooling layers found in standard deep convolutional neural networks. The main version of the proposed model uses mini-epitomes in place of filters and computes responses invariant to small translations by epitomic search instead of max-pooling over image positions. The topographic version of the proposed model uses large epitomes to learn filter maps organized in translational topographies. We show that error back-propagation can successfully learn multiple epitomic layers in a supervised fashion. The effectiveness of the proposed method is assessed in image classification tasks on standard benchmarks. Our experiments on Imagenet indicate improved recognition performance compared to standard convolutional neural networks of similar architecture. Our models pre-trained on Imagenet perform excellently on Caltech-101. We also obtain competitive image classification results on the small-image MNIST and CIFAR-10 datasets.

Connected through hyperedge HE-1 (induced by source paper "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"):
  with members:
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "visualizing and improving scattering networks"
    - "wide residual networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "learning from synthetic data using a stacked multichannel autoencoder"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "deep roto translation scattering for object classification"

Connected through hyperedge HE-2 (induced by source paper "good practice in cnn feature transfer"):
  with members:
    - "deep convolutional features for image based retrieval and scene categorization"
    - "exploiting local features from deep networks for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "a baseline for visual instance retrieval with deep convolutional networks"
    - "hypercolumns for object segmentation and fine grained localization"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "generalized max pooling"

Connected through hyperedge HE-3 (induced by source paper "imagenet large scale visual recognition challenge"):
  with members:
    - "cloudcv large scale distributed computer vision as a cloud service"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "densenet implementing efficient convnet descriptor pyramids"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "untangling local and global deformations in deep convolutional networks for image classification and sliding window detection"):
  with members:
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"
    - "deformable part models are convolutional neural networks"
    - "cnn features off the shelf an astounding baseline for recognition"

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)