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: "image denoising with multi layer perceptrons part 1 comparison with existing algorithms and with bounds"
  Abstract: Image denoising can be described as the problem of mapping from a noisy image to a noisefree image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with plain multi layer perceptrons (MLP) applied to image patches. We will show that by training on large image databases we are able to outperform the current state-of-the-art image denoising methods. In addition, our method achieves results that are superior to one type of theoretical bound and goes a large way toward closing the gap with a second type of theoretical bound. Our approach is easily adapted to less extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes, for which we achieve excellent results as well. We will show that combining a block-matching procedure with MLPs can further improve the results on certain images. In a second paper (Burger et al., 2012b), we detail the training trade-os and the inner mechanisms of our MLPs.

Connected through hyperedge HE-1 (induced by source paper "unsupervised image to image translation with self attention networks"):
  with members:
    - "arbitrary style transfer with style attentional networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data"):
  with members:
    - "deep autoassociative neural networks for noise reduction in seismic data"

Connected through hyperedge HE-3 (induced by source paper "deep joint rain detection and removal from a single image"):
  with members:
    - "deep edge guided recurrent residual learning for image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "dehazenet an end to end system for single image haze removal"
    - "a neural algorithm of artistic style"
    - "multi scale context aggregation by dilated convolutions"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "learning to deblur"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "joint rain detection and removal via iterative region dependent multi task learning"):
  with members:
    - "deep edge guided recurrent residual learning for image super resolution"
    - "dehazenet an end to end system for single image haze removal"
    - "a neural algorithm of artistic style"
    - "multi scale context aggregation by dilated convolutions"
    - "learning to deblur"
    - "image super resolution using deep convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "automatic photo adjustment using deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "image denoising with multi layer perceptrons part 2 training trade offs and analysis of their mechanisms"):
  with members:
    - "fast inference in sparse coding algorithms with applications to 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)