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: "multi frame quality enhancement for compressed video"
  Abstract: The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, ignoring the similarity between consecutive frames. In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as a first attempt in this direction. In our approach, we firstly develop a Support Vector Machine (SVM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are as the input. The MF-CNN compensates motion between the non-PQF and PQFs through the Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help of its nearest PQFs. Finally, the experiments validate the effectiveness and generality of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code of our MFQE approach is available at https://github.com/ryangBUAA/MFQE.git.

Connected through hyperedge HE-1 (induced by source paper "a foreground background parallel compression with residual encoding for surveillance video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "generative adversarial networks for extreme learned image compression"
    - "stochastic adversarial video prediction"
    - "progressive spatial recurrent neural network for intra prediction"
    - "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"
    - "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-3 (induced by source paper "video enhancement with task oriented flow"):
  with members:
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "context aware synthesis for video frame interpolation"
    - "flow guided feature aggregation for video object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "accurate optical flow via direct cost volume processing"
    - "deep burst denoising"

Connected through hyperedge HE-4 (induced by source paper "image and video compression with neural networks a review"):
  with members:
    - "learning for video compression"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "s net a scalable convolutional neural network for jpeg compression artifact reduction"
    - "reducing complexity of hevc a deep learning approach"
    - "spatially adaptive image compression using a tiled deep network"
    - "a practical convolutional neural network as loop filter for intra frame"
    - "convolutional neural network based block up sampling for intra frame coding"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"

Connected through hyperedge HE-5 (induced by source paper "mfqe 2 0 a new approach for multi frame quality enhancement on compressed video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "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-6 (induced by source paper "enhancing quality for vvc compressed videos by jointly exploiting spatial details and temporal structure"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "squeeze and excitation networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "real time video super resolution with spatio temporal networks and motion compensation"

Connected through hyperedge HE-7 (induced by source paper "quality gated convolutional lstm for enhancing compressed video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "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-8 (induced by source paper "mganet a robust model for quality enhancement of compressed video"):
  with members:
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "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"
    - "memnet a persistent memory network for image restoration"
    - "video frame synthesis using deep voxel flow"
    - "look listen and learn a multimodal lstm for speaker identification"

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)