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: "an end to end compression framework based on convolutional neural networks"
  Abstract: Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with state-of-the-art deblocking or denoising post-processing methods.

Connected through hyperedge HE-1 (induced by source paper "learning for video compression"):
  with members:
    - "learning based facial image compression with semantic fidelity metric"
    - "convolutional neural network based block up sampling for intra frame coding"
    - "a convolutional neural network approach for half pel interpolation in video coding"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "neural network based arithmetic coding of intra prediction modes in hevc"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"

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 "learning based facial image compression with semantic fidelity metric"):
  with members:
    - "learning for video compression"
    - "conditional probability models for deep image compression"
    - "guetzli perceptually guided jpeg encoder"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "learning to inpaint for image compression"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimization of nonlinear transform codes for perceptual quality"

Connected through hyperedge HE-4 (induced by source paper "cae p compressive autoencoder with pruning based on admm"):
  with members:
    - "conditional probability models for deep image compression"
    - "progressive weight pruning of deep neural networks using admm"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "learning convolutional networks for content weighted image compression"
    - "channel pruning for accelerating very deep neural networks"
    - "full resolution image compression with recurrent neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a deep image compression framework for face recognition"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "arcface additive angular margin loss for deep face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "full resolution image compression with recurrent neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "learning selection masks for deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "on the convergence of adam and beyond"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "deep saliency what is learnt by a deep network about saliency"
    - "densely connected convolutional networks"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "cae admm implicit bitrate optimization via admm based pruning in compressive autoencoders"):
  with members:
    - "progressive weight pruning of deep neural networks using admm"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "learning convolutional networks for content weighted image compression"
    - "channel pruning for accelerating very deep neural networks"
    - "full resolution image compression with recurrent neural networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "variable rate image compression with recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "faster and accurate classification for jpeg2000 compressed images in networked applications"):
  with members:
    - "towards image understanding from deep compression without decoding"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "wavelet convolutional neural networks for texture classification"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

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)