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 convolutional autoencoder based lossy image compression"
  Abstract: Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

Connected through hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-2 (induced by source paper "walking the tightrope an investigation of the convolutional autoencoder bottleneck"):
  with members:
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "deep image prior"
    - "instance normalization the missing ingredient for fast stylization"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep residual learning for image recognition"
    - "qualitatively characterizing neural network optimization problems"
    - "deep learning face attributes in the wild"
    - "generalized denoising auto encoders as generative models"

Connected through hyperedge HE-3 (induced by source paper "autoencoder based articulatory to acoustic mapping for ultrasound silent speech interfaces"):
  with members:
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "stylebank an explicit representation for neural image style transfer"
    - "lipreading with long short term memory"
    - "compressing word embeddings"
    - "deep learning and the information bottleneck principle"
    - "scheduled denoising autoencoders"

Connected through hyperedge HE-4 (induced by source paper "deep joint source channel coding for wireless image transmission"):
  with members:
    - "an autoencoder based learned image compressor description of challenge proposal by nctu"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep learning methods for improved decoding of linear codes"
    - "ai benchmark running deep neural networks on android smartphones"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)