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: "learning deep representations using convolutional auto encoders with symmetric skip connections"
  Abstract: Unsupervised pre-training was a critical technique for training deep neural networks years ago. With sufficient labeled data and modern training techniques, it is possible to train very deep neural networks from scratch in a purely supervised manner nowadays. However, unlabeled data is easier to obtain and usually of very large scale. How to make use of them better to help supervised learning is still a well-valued topic. In this paper, we investigate convolutional denoising auto-encoders to show that unsupervised pre-training can still improve the performance of high-level image related tasks such as image classification and semantic segmentation. The architecture we use is a convolutional auto-encoder network with symmetric shortcut connections. We empirically show that symmetric shortcut connections are very important for learning abstract representations via image reconstruction. When no extra unlabeled data are available, unsupervised pre-training with our network can regularize the supervised training and therefore lead to better generalization performance. With the help of unsupervised pre-training, our method achieves very competitive results in image classification using very simple all-convolution networks. When labeled data are limited but extra unlabeled data are available, our method achieves good results in several semi-supervised learning tasks.

Connected through hyperedge HE-1 (induced by source paper "s net a scalable convolutional neural network for jpeg compression artifact reduction"):
  with members:
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "enhanced deep residual networks for single image super resolution"
    - "single image super resolution with dilated convolution based multi scale information learning inception module"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep convolution networks for compression artifacts reduction"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-2 (induced by source paper "privynet a flexible framework for privacy preserving deep neural network training"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "channel pruning for accelerating very deep neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "pruning convolutional neural networks for resource efficient inference"
    - "pruning filters for efficient convnets"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image 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)