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: "advancements in image classification using convolutional neural network"
  Abstract: Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet model. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.

Connected through hyperedge HE-1 (induced by source paper "self supervised visual feature learning with curriculum"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "automated curriculum learning for neural networks"
    - "cassl curriculum accelerated self supervised learning"
    - "learning representations for automatic colorization"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-2 (induced by source paper "a review of object detection models based on convolutional neural network"):
  with members:
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "understanding batch normalization"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "hard negative mining for metric learning based zero shot classification"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "bdnet bengali handwritten numeral digit recognition based on densely connected convolutional neural networks"):
  with members:
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "dynamic routing between capsules"
    - "squeeze and excitation networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

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)