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: "convolutional sparse coding for high dynamic range imaging"
  Abstract: Current HDR acquisition techniques are based on either i fusing multibracketed, low dynamic range LDR images, ii modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or iii reconstructing a single image with spatially-varying pixel exposures. In this paper, we propose a novel algorithm to recover high-quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently-introduced ideas of convolutional sparse coding CSC; this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher-quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.

Connected through hyperedge HE-1 (induced by source paper "adversarial defense by stratified convolutional sparse coding"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "divide denoise and defend against adversarial attacks"
    - "sok security and privacy in machine learning"

Connected through hyperedge HE-2 (induced by source paper "convolutional dictionary learning a comparative review and new algorithms"):
  with members:
    - "convolutional dictionary learning acceleration and convergence"
    - "first and second order methods for online convolutional dictionary learning"
    - "synthesis versus analysis in patch based image priors"
    - "online convolutional dictionary learning for multimodal imaging"
    - "online convolutional dictionary learning"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-3 (induced by source paper "convolutional dictionary learning acceleration and convergence"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "optimization methods for convolutional sparse coding"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"
    - "fast stochastic alternating direction method of multipliers"

Connected through hyperedge HE-4 (induced by source paper "convolutional sparse coding for capturing high speed video content"):
  with members:
    - "optimization methods for convolutional sparse coding"
    - "convolutional matching pursuit and dictionary training"
    - "compressed compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "hdr image reconstruction from a single exposure using deep cnns"):
  with members:
    - "learning high dynamic range from outdoor panoramas"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "compression artifacts removal using convolutional neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "context encoders feature learning by inpainting"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "deep high dynamic range imaging with large foreground motions"):
  with members:
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep cascaded bi network for face hallucination"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "flownet learning optical flow with convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "scalable online convolutional sparse coding"):
  with members:
    - "online convolutional dictionary learning for multimodal imaging"
    - "online convolutional dictionary learning"

Connected through hyperedge HE-8 (induced by source paper "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "reconstruction free action inference from compressive imagers"
    - "compressing convolutional neural networks"
    - "towards trainable media using waves for neural network style training"

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)