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: "learnable image encryption"
  Abstract: The network-based machine learning algorithm is very powerful tools. However, it requires huge training dataset. Researchers often meet privacy issues when they collect image dataset especially for surveillance applications. A learnable image encryption scheme is introduced. The key idea of this scheme is to encrypt images, so that human cannot understand images but the network can be train with encrypted images. This scheme allows us to train the network without the privacy issues. In this paper, a simple learnable image encryption algorithm is proposed. Then, the proposed algorithm is validated with cifar dataset.

Connected through hyperedge HE-1 (induced by source paper "block wise scrambled image recognition using adaptation network"):
  with members:
    - "autoshufflenet learning permutation matrices via an exact lipschitz continuous penalty in deep convolutional neural networks"
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "shakedrop regularization for deep residual learning"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving deep neural networks with pixel based image encryption considering data augmentation in the encrypted domain"):
  with members:
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "random erasing data augmentation"
    - "mixup beyond empirical risk minimization"
    - "deep residual learning for image recognition"
    - "deep learning and the information bottleneck principle"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "on the security of pixel based image encryption for privacy preserving deep neural networks"):
  with members:
    - "privacy preserving deep neural networks with pixel based image encryption considering data augmentation in the encrypted domain"
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "deep learning and the information bottleneck principle"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving machine learning using etc images"):
  with members:
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "grayscale based block scrambling image encryption for social networking services"

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)