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 cropping with composition and saliency aware aesthetic score map"):
  Abstract: Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.

Includes members:
  Member "listwise view ranking for image cropping":
    also connected through other hyperedges:
      (none)
  Member "effective aesthetics prediction with multi level spatially pooled features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sur featnet predicting the satisfied user ratio curvefor image compression with deep feature learning")
  Member "reliable and efficient image cropping a grid anchor based approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grid anchor based image cropping a new benchmark and an efficient model")
  Member "an end to end neural network for image cropping by learning composition from aesthetic photos":
    also connected through other hyperedges:
      (none)
  Member "quantitative analysis of automatic image cropping algorithms a dataset and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an end to end neural network for image cropping by learning composition from aesthetic photos")
  Member "learning to compose with professional photographs on the web":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "composition aware image aesthetics assessment")
  Member "a2 rl aesthetics aware reinforcement learning for image cropping":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation")

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)