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 "coconet a deep neural network for mapping pixel coordinates to color values"):
  Abstract: In this paper, we propose a deep neural network approach for mapping the 2D pixel coordinates in an image to the corresponding Red-Green-Blue (RGB) color values. The neural network is termed CocoNet, i.e. coordinates-to-color network. During the training process, the neural network learns to encode the input image within its layers. More specifically, the network learns a continuous function that approximates the discrete RGB values sampled over the discrete 2D pixel locations. At test time, given a 2D pixel coordinate, the neural network will output the approximate RGB values of the corresponding pixel. By considering every 2D pixel location, the network can actually reconstruct the entire learned image. It is important to note that we have to train an individual neural network for each input image, i.e. one network encodes a single image only. To the best of our knowledge, we are the first to propose a neural approach for encoding images individually, by learning a mapping from the 2D pixel coordinate space to the RGB color space. Our neural image encoding approach has various low-level image processing applications ranging from image encoding, image compression and image denoising to image resampling and image completion. We conduct experiments that include both quantitative and qualitative results, demonstrating the utility of our approach and its superiority over standard baselines, e.g. bilateral filtering or bicubic interpolation. Our code is available at this https URL.

Includes members:
  Member "spatially adaptive image compression using a tiled deep network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study")
  Member "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rrpn radar region proposal network for object detection in autonomous vehicles")
  Member "cas cnn a deep convolutional neural network for image compression artifact suppression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning based video coding a review and a case study")
  Member "how hard can it be estimating the difficulty of visual search in an image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "fast and accurate image super resolution by deep cnn with skip connection and network in network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learned multi view texture super resolution")
  Member "learning to inpaint for image compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning for video compression")
  Member "full resolution image compression with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "generic 3d convolutional fusion for image restoration":
    also connected through other hyperedges:
      (none)

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)