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: "enhancing quality for hevc compressed videos"
  Abstract: The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to generate video streams over the Internet. However, HEVC compressed videos may incur severe quality degradation, particularly at low bit rates. Thus, it is necessary to enhance the visual quality of HEVC videos at the decoder side. To this end, this paper proposes a quality enhancement convolutional neural network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC. In particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the distortion of HEVC I and P/B frames, respectively. The proposed method differs from the existing CNN-based quality enhancement approaches, which only handle intra-coding distortion and are thus not suitable for P/B frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P/B frames of HEVC videos. To apply our QE-CNN method in time-constrained scenarios, we further propose a time-constrained quality enhancement optimization (TQEO) scheme. Our TQEO scheme controls the computational time of QE-CNN to meet a target, meanwhile maximizing the quality enhancement. Next, the experimental results demonstrate the effectiveness of our TQEO scheme from the aspects of time control accuracy and quality enhancement under different time constraints. Finally, we design a prototype to implement our TQEO scheme in a real-time scenario.

Connected through hyperedge HE-1 (induced by source paper "a foreground background parallel compression with residual encoding for surveillance video"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "stochastic adversarial video prediction"
    - "progressive spatial recurrent neural network for intra prediction"
    - "multi frame quality enhancement for compressed video"
    - "video compression through image interpolation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "image segmentation in video sequences a probabilistic approach"

Connected through hyperedge HE-2 (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"
    - "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"
    - "deep convolutional autoencoder based lossy image compression"

Connected through hyperedge HE-3 (induced by source paper "mfqe 2 0 a new approach for multi frame quality enhancement on compressed video"):
  with members:
    - "multi frame quality enhancement for compressed video"
    - "saliency guided complexity control for hevc decoding"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "detail revealing deep video super resolution"
    - "end to end learning of video super resolution with motion compensation"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-4 (induced by source paper "quality gated convolutional lstm for enhancing compressed video"):
  with members:
    - "multi frame quality enhancement for compressed video"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "compression artifacts reduction by a deep convolutional network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "a densenet based approach for multi frame in loop filter in hevc"):
  with members:
    - "reducing complexity of hevc a deep learning approach"
    - "convolutional pose machines"
    - "densely connected convolutional networks"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "multi frame quality enhancement for compressed video"):
  with members:
    - "saliency guided complexity control for hevc decoding"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "end to end learning of video super resolution with motion compensation"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "deep residual learning for image recognition"
    - "compression artifacts reduction by a deep convolutional network"

Connected through hyperedge HE-7 (induced by source paper "what makes natural scene memorable"):
  with members:
    - "deep visual attention prediction"
    - "reducing complexity of hevc a deep learning approach"
    - "multi frame quality enhancement for compressed video"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "enhancing hevc compressed videos with a partition masked convolutional neural network"):
  with members:
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "compression artifacts reduction by a deep convolutional network"

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.MM(Multimedia)