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: "a survey of recent advances in texture representation"
  Abstract: Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention. Since 2000, texture representations based on Bag of Words (BoW) and on Convolutional Neural Networks (CNNs) have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 200 major publications are cited in this survey covering different aspects of the research, which includes (i) problem description; (ii) recent advances in the broad categories of BoW-based, CNN-based and attribute-based methods; and (iii) evaluation issues, specifically benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research.

Connected through hyperedge HE-1 (induced by source paper "texture retrieval in the wild through detection based attributes"):
  with members:
    - "texel att representing and classifying element based textures by attributes"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "visual search at ebay"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "mask r cnn"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "deep filter banks for texture recognition description and segmentation"

Connected through hyperedge HE-2 (induced by source paper "texel att representing and classifying element based textures by attributes"):
  with members:
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "mask r cnn"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "deep filter banks for texture recognition description and segmentation"
    - "describing textures in the wild"

Connected through hyperedge HE-3 (induced by source paper "denoising prior driven deep neural network for image restoration"):
  with members:
    - "deep learning for generic object detection a survey"
    - "image super resolution via dual state recurrent networks"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "image restoration using autoencoding priors"

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)