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: "dual residual networks leveraging the potential of paired operations for image restoration"
  Abstract: In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of paired operations, e.g., up- and down-sampling or convolution with large- and small-size kernels. We design a modular block implementing this connection style; it is equipped with two containers to which arbitrary paired operations are inserted. Adopting the "unraveled" view of the residual networks proposed by Veit et al., we point out that a stack of the proposed modular blocks allows the first operation in a block interact with the second operation in any subsequent blocks. Specifying the two operations in each of the stacked blocks, we build a complete network for each individual task of image restoration. We experimentally evaluate the proposed approach on five image restoration tasks using nine datasets. The results show that the proposed networks with properly chosen paired operations outperform previous methods on almost all of the tasks and datasets.

Connected through hyperedge HE-1 (induced by source paper "memory efficient hierarchical neural architecture search for image denoising"):
  with members:
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "darts improved differentiable architecture search with early stopping"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "graph hypernetworks for neural architecture search"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-2 (induced by source paper "restoring images with unknown degradation factors by recurrent use of a multi branch network"):
  with members:
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "pyramid real image denoising network"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "selective kernel networks"
    - "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)