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: "reducing complexity of hevc a deep learning approach"
  Abstract: High efficiency video coding (HEVC) significantly reduces bit rates over the preceding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of the coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra-and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network. First, we establish a large-scale database including substantial CU partition data for the HEVC intra- and inter-modes. This enables deep learning on the CU partition. Second, we represent the CU partition of an entire coding tree unit in the form of a hierarchical CU partition map (HCPM). Then, we propose an early terminated hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the encoding complexity of intra-mode HEVC can be drastically reduced by replacing the brute-force search with ETH-CNN to decide the CU partition. Third, an ETH-LSTM is proposed to learn the temporal correlation of the CU partition. Then, we combine the ETH-LSTM and the ETH-CNN to predict the CU partition for reducing the HEVC complexity at inter-mode. Finally, experimental results show that our approach outperforms the other state-of-the-art approaches in reducing the HEVC complexity at both intra- and inter-modes.

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 "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"
    - "multi frame quality enhancement for compressed video"
    - "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-3 (induced by source paper "deep reference generation with multi domain hierarchical constraints for inter prediction"):
  with members:
    - "video enhancement with task oriented flow"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a convolutional neural network approach for half pel interpolation in video coding"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-4 (induced by source paper "enhancing quality for hevc compressed videos"):
  with members:
    - "saliency guided complexity control for hevc decoding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "mathbf d 3 deep dual domain based fast restoration of jpeg compressed images"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "compression artifacts reduction by a deep convolutional network"

Connected through hyperedge HE-5 (induced by source paper "a densenet based approach for multi frame in loop filter in hevc"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "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 "understanding and predicting the memorability of outdoor natural scenes"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "salient object detection in the deep learning era an in depth survey"
    - "deep visual attention prediction"
    - "what makes natural scene memorable"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "what makes natural scene memorable"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "deep visual attention prediction"
    - "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"

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)