human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "flickr1024 a dataset for stereo image super resolution"):
  Abstract: With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs. However, the lack of high-quality stereo datasets has limited the research in this area. To facilitate the training and evaluation of novel stereo SR algorithms, in this paper, we propose a large-scale stereo dataset named Flickr1024. Compared to the existing stereo datasets, the proposed dataset contains much more high-quality images and covers diverse scenarios. We train two state-of-the-art stereo SR methods (i.e., StereoSR and PASSRnet) on the KITTI2015, Middlebury, and Flickr1024 datasets. Experimental results demonstrate that our dataset can improve the performance of stereo SR algorithms. The Flickr1024 dataset is available online at: this https URL.

Includes members:
  Member "learning parallax attention for stereo image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery")
  Member "video enhancement with task oriented flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "softmax splatting for video frame interpolation")
  Member "image super resolution using very deep residual channel attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "no reference color image quality assessment from entropy to perceptual quality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flickr1024 a large scale dataset for stereo image super resolution")
  Member "learning for disparity estimation through feature constancy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "learning a no reference quality metric for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mri super resolution with gan and 3d multi level densenet smaller faster and better")

Question: Please tell me which class the source paper 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)