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: "crossnet an end to end reference based super resolution network using cross scale warping"
  Abstract: The Reference-based Super-resolution (RefSR) super-resolves a low-resolution (LR) image given an external high-resolution (HR) reference image, where the reference image and LR image share similar viewpoint but with significant resolution gap x8. Existing RefSR methods work in a cascaded way such as patch matching followed by synthesis pipeline with two independently defined objective functions, leading to the inter-patch misalignment, grid effect and inefficient optimization. To resolve these issues, we present CrossNet, an end-to-end and fully-convolutional deep neural network using cross-scale warping. Our network contains image encoders, cross-scale warping layers, and fusion decoder: the encoder serves to extract multi-scale features from both the LR and the reference images; the cross-scale warping layers spatially aligns the reference feature map with the LR feature map; the decoder finally aggregates feature maps from both domains to synthesize the HR output. Using cross-scale warping, our network is able to perform spatial alignment at pixel-level in an end-to-end fashion, which improves the existing schemes both in precision (around 2dB-4dB) and efficiency (more than 100 times faster).

Connected through hyperedge HE-1 (induced by source paper "extended feature pyramid network for small object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "image super resolution by neural texture transfer"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "image super resolution by neural texture transfer"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution via dual state recurrent networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "multi scale dual branch fully convolutional network for hand parsing"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "progressive refinement a method of coarse to fine image parsing using stacked network"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning to estimate 3d hand pose from single rgb images"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "learning high fidelity light field images from hybrid inputs"):
  with members:
    - "deep learning for image super resolution a survey"
    - "geometry consistent light field super resolution via graph based regularization"
    - "residual dense network for image super resolution"
    - "light field denoising via anisotropic parallax analysis in a cnn framework"
    - "light field blind motion deblurring"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a 4d light field dataset and cnn architectures for material recognition"

Connected through hyperedge HE-5 (induced by source paper "lapepi net a laplacian pyramid epi structure for learning based dense light field reconstruction"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning based view synthesis for light field cameras"
    - "light field reconstruction using shearlet transform"
    - "deepstereo learning to predict new views from the world s imagery"
    - "adam a method for stochastic optimization"

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)