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: "path restore learning network path selection for image restoration"
  Abstract: Very deep Convolutional Neural Networks (CNNs) have greatly improved the performance on various image restoration tasks. However, this comes at a price of increasing computational burden, which limits their practical usages. We believe that some corrupted image regions are inherently easier to restore than others since the distortion and content vary within an image. To this end, we propose Path-Restore, a multi-path CNN with a pathfinder that could dynamically select an appropriate route for each image region. We train the pathfinder using reinforcement learning with a difficulty-regulated reward, which is related to the performance, complexity and "the difficulty of restoring a region". We conduct experiments on denoising and mixed restoration tasks. The results show that our method could achieve comparable or superior performance to existing approaches with less computational cost. In particular, our method is effective for real-world denoising, where the noise distribution varies across different regions of a single image. We surpass the state-of-the-art CBDNet by 0.94 dB and run 29% faster on the realistic Darmstadt Noise Dataset. Models and codes will be released.

Connected through hyperedge HE-1 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution"):
  with members:
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "attention based adaptive selection of operations for image restoration in the presence of unknown combined distortions"
    - "mutan multimodal tucker fusion for visual question answering"
    - "single image super resolution via a lightweight residual convolutional neural network"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-2 (induced by source paper "when awgn based denoiser meets real noises"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "real image denoising with feature attention"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "survey of face detection on low quality images"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-3 (induced by source paper "edvr video restoration with enhanced deformable convolutional networks"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "adversarial spatio temporal learning for video deblurring"
    - "video enhancement with task oriented flow"
    - "frame recurrent video super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "object detection in video with spatiotemporal sampling networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "scale recurrent network for deep image deblurring"

Connected through hyperedge HE-4 (induced by source paper "pyramid real image denoising network"):
  with members:
    - "selective kernel networks"
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "squeeze and excitation networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

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)