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: "learned spectral super resolution"
  Abstract: We describe a novel method for blind, single-image spectral super-resolution. While conventional super-resolution aims to increase the spatial resolution of an input image, our goal is to spectrally enhance the input, i.e., generate an image with the same spatial resolution, but a greatly increased number of narrow (hyper-spectral) wave-length bands. Just like the spatial statistics of natural images has rich structure, which one can exploit as prior to predict high-frequency content from a low resolution image, the same is also true in the spectral domain: the materials and lighting conditions of the observed world induce structure in the spectrum of wavelengths observed at a given pixel. Surprisingly, very little work exists that attempts to use this diagnosis and achieve blind spectral super-resolution from single images. We start from the conjecture that, just like in the spatial domain, we can learn the statistics of natural image spectra, and with its help generate finely resolved hyper-spectral images from RGB input. Technically, we follow the current best practice and implement a convolutional neural network (CNN), which is trained to carry out the end-to-end mapping from an entire RGB image to the corresponding hyperspectral image of equal size. We demonstrate spectral super-resolution both for conventional RGB images and for multi-spectral satellite data, outperforming the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "accurate spectral super resolution from single rgb image using multi scale cnn"):
  with members:
    - "adaptive importance learning for improving lightweight image super resolution network"
    - "beyond low rank a data adaptive tensor completion method"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "hyperspectral pansharpening a review"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "towards spectral estimation from a single rgb image in the wild"):
  with members:
    - "accurate spectral super resolution from single rgb image using multi scale cnn"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "hyperspectral recovery from rgb images using gaussian processes"):
  with members:
    - "adversarial networks for spatial context aware spectral image reconstruction from rgb"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-4 (induced by source paper "adversarial networks for spatial context aware spectral image reconstruction from rgb"):
  with members:
    - "towards adversarial retinal image synthesis"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning what and where to draw"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"

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)