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: "encryption then compression systems using grayscale based image encryption for jpeg images"
  Abstract: A block scrambling-based encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems with JPEG compression, which allow us to securely transmit images through an untrusted channel provider, such as social network service providers. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the conventional scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. In an experiment, the security against jigsaw puzzle solver attacks is evaluated. Encrypted images were uploaded to and then downloaded from Facebook and Twitter, and the results demonstrated that the proposed scheme is effective for EtC systems.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups"):
  with members:
    - "learning to compress and search visual data in large scale systems"
    - "generative adversarial networks for extreme learned image compression"
    - "context aware generative adversarial privacy"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "defeating image obfuscation with deep learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (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"
    - "learnable image encryption"
    - "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-3 (induced by source paper "privacy preserving deep neural networks with pixel based image encryption considering data augmentation in the encrypted domain"):
  with members:
    - "learnable image encryption"
    - "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-4 (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"
    - "learnable image encryption"
    - "deep learning and the information bottleneck principle"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving machine learning using etc images"):
  with members:
    - "learnable image encryption"
    - "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.CR(Cryptography and Security)