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: "deep learning for image denoising a survey"
  Abstract: Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.

Connected through hyperedge HE-1 (induced by source paper "blind universal bayesian image denoising with gaussian noise level learning"):
  with members:
    - "class aware fully convolutional gaussian and poisson denoising"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "unprocessing images for learned raw denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-2 (induced by source paper "a simple and effective postprocessing method for image classification"):
  with members:
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "dropblock a regularization method for convolutional networks"
    - "normface l 2 hypersphere embedding for face verification"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "a latent variable model approach to pmi based word embeddings"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "enhanced cnn for image denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "multi level wavelet cnn for image restoration"
    - "a cascaded convolutional neural network for x ray low dose ct image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "block matching convolutional neural network for image denoising"

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)