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: "weakly and self supervised learning for content aware deep image retargeting"
  Abstract: This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.

Connected through hyperedge HE-1 (induced by source paper "deep video inpainting"):
  with members:
    - "video to video synthesis"
    - "image inpainting for irregular holes using partial convolutions"
    - "learning blind video temporal consistency"
    - "free form image inpainting with gated convolution"
    - "youtube vos sequence to sequence video object segmentation"
    - "generative image inpainting with contextual attention"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "image resizing by reconstruction from deep features"):
  with members:
    - "deepir a deep semantics driven framework for image retargeting"
    - "the perception distortion tradeoff"
    - "visual attribute transfer through deep image analogy"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "context aware saliency detection for image retargeting using convolutional neural networks"):
  with members:
    - "image retargetability"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "fast at fast automatic thumbnail generation using deep neural networks"
    - "pyramid scene parsing network"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "pisa pixelwise image saliency by aggregating complementary appearance contrast measures with edge preserving coherence"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "cycle ir deep cyclic image retargeting"):
  with members:
    - "image retargeting via beltrami representation"
    - "deep contrast learning for salient object detection"
    - "unsupervised monocular depth estimation with left right consistency"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "image seam carving by controlling positional distribution of seams"):
  with members:
    - "fast at fast automatic thumbnail generation using deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "hallucinated iqa no reference image quality assessment via adversarial learning"):
  with members:
    - "on the use of deep learning for blind image quality assessment"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "quality aware network for set to set recognition"
    - "learning deep cnn denoiser prior for image restoration"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "stacked hourglass networks for human pose estimation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-7 (induced by source paper "ingan capturing and remapping the dna of a natural image"):
  with members:
    - "non stationary texture synthesis by adversarial expansion"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the role of minimal complexity functions in unsupervised learning of semantic mappings"
    - "deep image prior"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "zero shot super resolution using deep internal learning"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-8 (induced by source paper "deepir a deep semantics driven framework for image retargeting"):
  with members:
    - "multi scale face restoration with sequential gating ensemble network"
    - "conditional image to image translation"
    - "visual attribute transfer through deep image analogy"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)