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: "learning parallax attention for stereo image super resolution"
  Abstract: Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attention stereo superresolution network (PASSRnet) to integrate the information from a stereo image pair for SR. Specifically, we introduce a parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations. We also propose a new and the largest dataset for stereo image SR (namely, Flickr1024). Extensive experiments demonstrate that the parallax-attention mechanism can capture correspondence between stereo images to improve SR performance with a small computational and memory cost. Comparative results show that our PASSRnet achieves the state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets.

Connected through hyperedge HE-1 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery"):
  with members:
    - "tackling climate change with machine learning"
    - "frame and feature context video super resolution"
    - "deep learning for image super resolution a survey"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "learning disentangled representations of satellite image time series"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "mapping informal settlements in developing countries with multi resolution multi spectral data"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "flickr1024 a dataset for stereo image super resolution"):
  with members:
    - "video enhancement with task oriented flow"
    - "image super resolution using very deep residual channel attention networks"
    - "no reference color image quality assessment from entropy to perceptual quality"
    - "learning for disparity estimation through feature constancy"
    - "learning a no reference quality metric for single image super resolution"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks a binocular vision perspective"):
  with members:
    - "flickr1024 a large scale dataset for stereo image super resolution"
    - "gabor convolutional networks"
    - "dynamic routing between capsules"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "analyzing the performance of multilayer neural networks for object recognition"

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)