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 2 training trade offs and analysis of their mechanisms"
  Abstract: Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. In Burger et al. (2012), we show that multi-layer perceptrons can achieve outstanding image denoising performance for various types of noise (additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes). In this work we discuss in detail which trade-os have to be considered during the training procedure. We will show how to achieve good results and which pitfalls to avoid. By analysing the activation patterns of the hidden units we are able to make observations regarding the functioning principle of multi-layer perceptrons trained for image denoising.

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 "progressive sparse local attention for video object detection"):
  with members:
    - "ocnet object context network for scene parsing"
    - "dynamic video segmentation network"
    - "ccnet criss cross attention for semantic segmentation"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "dual attention network for scene segmentation"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "non local neural networks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "visual interaction networks"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation networks"
    - "the kinetics human action video dataset"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-4 (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-5 (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"

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)