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: "is image super resolution helpful for other vision tasks"
  Abstract: Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge detection, semantic image segmentation, digit recognition, and scene recognition. We show that applying ISR to input images of other vision systems does improve their performance when the input images are of low-resolution. We also study the correlation between four standard perceptual evaluation criteria (namely PSNR, SSIM, IFC, and NQM) and the usefulness of ISR to the vision tasks. Experiments show that they correlate well with each other in general, but perceptual criteria are still not accurate enough to be used as full proxies for the usefulness. We hope this work will inspire the community to evaluate ISR methods also in real vision applications, and to adopt ISR as a pre-processing step of other vision tasks if the resolution of their input images is low.

Connected through hyperedge HE-1 (induced by source paper "deep video super resolution using hr optical flow estimation"):
  with members:
    - "frame recurrent video super resolution"
    - "scale recurrent network for deep image deblurring"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning for video super resolution through hr optical flow estimation"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"

Connected through hyperedge HE-2 (induced by source paper "two stream action recognition oriented video super resolution"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "the perception distortion tradeoff"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "enhanced deep residual networks for single image super resolution"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-3 (induced by source paper "the effects of super resolution on object detection performance in satellite imagery"):
  with members:
    - "satellite imagery multiscale rapid detection with windowed networks"
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "task driven super resolution object detection in low resolution images"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (induced by source paper "toward bridging the simulated to real gap benchmarking super resolution on real data"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "benchmarking denoising algorithms with real photographs"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "convolutional low resolution fine grained classification"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "fine to coarse knowledge transfer for low res image classification"
    - "studying very low resolution recognition using deep networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "deeply recursive convolutional network for image super resolution"

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)