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 "a unified framework for salient structure detection by contour guided visual search"):
  Abstract: We define the task of salient structure (SS) detection to unify the saliency-related tasks, such as fixation prediction, salient object detection, and detection of other structures of interest in cluttered environments. To solve such SS detection tasks, a unified framework inspired by the two-pathway-based search strategy of biological vision is proposed in this paper. First, a contour-based spatial prior (CBSP) is extracted based on the layout of edges in the given scene along a fast non-selective pathway, which provides a rough, task-irrelevant, and robust estimation of the locations where the potential SSs are present. Second, another flow of local feature extraction is executed in parallel along the selective pathway. Finally, Bayesian inference is used to auto-weight and integrate the local cues guided by CBSP and to predict the exact locations of SSs. This model is invariant to the size and features of objects. The experimental results on six large datasets (three fixation prediction datasets and three salient object datasets) demonstrate that our system achieves competitive performance for SS detection (i.e., both the tasks of fixation prediction and salient object detection) compared with the state-of-the-art methods. In addition, our system also performs well for salient object construction from saliency maps and can be easily extended for salient edge detection.

Includes members:
  Member "salient object detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross layer feature pyramid network for salient object detection")
  Member "the secrets of salient object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "visual saliency based on scale space analysis in the frequency domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")

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)