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: "from bits to images inversion of local binary descriptors"
  Abstract: Local Binary Descriptors are becoming more and more popular for image matching tasks, especially when going mobile. While they are extensively studied in this context, their ability to carry enough information in order to infer the original image is seldom addressed. #R##N#In this work, we leverage an inverse problem approach to show that it is possible to directly reconstruct the image content from Local Binary Descriptors. This process relies on very broad assumptions besides the knowledge of the pattern of the descriptor at hand. This generalizes previous results that required either a prior learning database or non-binarized features. #R##N#Furthermore, our reconstruction scheme reveals differences in the way different Local Binary Descriptors capture and encode image information. Hence, the potential applications of our work are multiple, ranging from privacy issues caused by eavesdropping image keypoints streamed by mobile devices to the design of better descriptors through the visualization and the analysis of their geometric content.

Connected through hyperedge HE-1 (induced by source paper "perceptual losses for real time style transfer and super resolution"):
  with members:
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "colorful image colorization"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "inverting visual representations with convolutional networks"):
  with members:
    - "image reconstruction from bag of visual words"
    - "understanding deep image representations by inverting them"
    - "adam a method for stochastic optimization"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "striving for simplicity the all convolutional net"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning to generate chairs with convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "optimising spatial and tonal data for pde based inpainting"):
  with members:
    - "a bi level view of inpainting based image compression"
    - "anisotropic mesh adaptation for image representation and scaling"

Connected through hyperedge HE-4 (induced by source paper "seeing through bag of visual word glasses towards understanding quantization effects in feature extraction methods"):
  with members:
    - "image reconstruction from bag of visual words"

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)