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 "weakly supervised learning for salient object detection using background images"):
  Abstract: Recent advances of supervised salient object detection models demonstrate significant performance on benchmark datasets. Training such models, however, requires expensive pixel-wise annotations of salient objects. Moreover, many existing salient object detection models assume that at least a salient object exists in the input image. Such an impractical assumption leads to less appealing saliency maps on the background images, which contain no salient objects at all. #R##N#To avoid expensive strong saliency annotations, in this paper, we study weakly supervised learning approaches for salient object detection. In specific, given a set of background images and/or salient object images, where we only have annotations of salient object existence, we propose two approaches to train salient object detection models. In the first approach, we train a one-class SVM based on background superpixels. The further a superpixel is from the decision boundary of the one-class SVM, the more salient it is. The most interesting property of this approach is that we can effortlessly synthesize a set of background images to train the model. In the second approach, we present a solution toward jointly addressing salient object existence and detection tasks. We formulate salient object detection as an image labeling problem, where saliency labels of superpixels are modeled as hidden variables in the latent structural SVM framework. Experimental results on benchmark datasets validate the effectiveness of our proposed approaches.

Includes members:
  Member "salient object detection a benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "describing textures in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)