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 "compare and contrast learning prominent visual differences"):
  Abstract: Relative attribute models can compare images in terms of all detected properties or attributes, exhaustively predicting which image is fancier, more natural, and so on without any regard to ordering. However, when humans compare images, certain differences will naturally stick out and come to mind first. These most noticeable differences, or prominent differences, are likely to be described first. In addition, many differences, although present, may not be mentioned at all. In this work, we introduce and model prominent differences, a rich new functionality for comparing images. We collect instance-level annotations of most noticeable differences, and build a model trained on relative attribute features that predicts prominent differences for unseen pairs. We test our model on the challenging UT-Zap50K shoes and LFW10 faces datasets, and outperform an array of baseline methods. We then demonstrate how our prominence model improves two vision tasks, image search and description generation, enabling more natural communication between people and vision systems.

Includes members:
  Member "learning the latent look unsupervised discovery of a style coherent embedding from fashion images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "fashion forward forecasting visual style in fashion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "robust subjective visual property prediction from crowdsourced pairwise labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable bayesian preference learning for crowds")
  Member "shallow and deep convolutional networks for saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "end to end localization and ranking for relative attributes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on symbiosis of attribute prediction and semantic segmentation")
  Member "whittlesearch interactive image search with relative attribute feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dressing for diverse body shapes")
  Member "generation and comprehension of unambiguous object descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "understanding image virality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "integrating text and image determining multimodal document intent in instagram posts")

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)