human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "image retargetability"):
  Abstract: Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all images can be equally well processed that way. In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated retargetability rating problem. To train and analyze this model, we have collected a database which contains retargetability scores and meaningful image attributes assigned by six expert raters. Experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show applications of image retargetability in retargeting method selection, retargeting method assessment and photo collage generation.

Includes members:
  Member "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quality aware unpaired image to image translation")
  Member "learning to compose with professional photographs on the web":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an end to end neural network for image cropping by learning composition from aesthetic photos")
  Member "deep cropping via attention box prediction and aesthetics assessment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "composition aware image aesthetics assessment")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "learning to compare image patches via convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "multi task cnn model for attribute prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pedestrian attribute recognition a survey")
  Member "mirror mirror on the wall tell me is the error small":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)