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: "large receptive field networks for high scale image super resolution"
  Abstract: Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "residual bi fusion feature pyramid network for accurate single shot object detection"):
  with members:
    - "making convolutional networks shift invariant again"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "augmentation for small object detection"
    - "cornernet detecting objects as paired keypoints"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "yolov3 an incremental improvement"
    - "cfenet an accurate and efficient single shot object detector for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "s flow gan"):
  with members:
    - "quantifying the reality gap in robotic manipulation tasks"
    - "the perception distortion tradeoff"
    - "banach wasserstein gan"
    - "towards accurate generative models of video a new metric challenges"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "pixel aware deep function mixture network for spectral super resolution"):
  with members:
    - "residual dense network for image super resolution"
    - "adversarial networks for spatial context aware spectral image reconstruction from rgb"
    - "deformable convolutional networks"
    - "mask r cnn"
    - "dynamic filter networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "encoder decoder based cnn and fully connected crfs for remote sensed image segmentation"):
  with members:
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "learning to refine object segments"
    - "multi scale context aggregation by dilated convolutions"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)