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 "relative saliency and ranking models metrics data and benchmarks"):
  Abstract: Salient object detection is a problem that has been considered in detail and \textcolor{black}{many solutions have been proposed}. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. Initially, we present a novel deep learning solution based on a hierarchical representation of relative saliency and stage-wise refinement. Further to this, we present data, analysis and baseline benchmark results towards addressing the problem of salient object ranking. Methods for deriving suitable ranked salient object instances are presented, along with metrics suitable to measuring algorithm performance. In addition, we show how a derived dataset can be successively refined to provide cleaned results that correlate well with pristine ground truth in its characteristics and value for training and testing models. Finally, we provide a comparison among prevailing algorithms that address salient object ranking or detection to establish initial baselines providing a basis for comparison with future efforts addressing this problem. \textcolor{black}{The source code and data are publicly available via our project page:} \textrm{\href{https://ryersonvisionlab.github.io/cocosalrank.html}{ryersonvisionlab.github.io/cocosalrank}}

Includes members:
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "derivative manipulation for general example weighting")
  Member "gated feedback refinement network for coarse to fine dense semantic image labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient ladder style densenets for semantic segmentation of large images")
  Member "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross layer feature pyramid network for salient object detection")
  Member "reverse attention for salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "learning uncertain convolutional features for accurate saliency detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "towards the success rate of one real time unconstrained salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting lesion bounding ellipses with gaussian proposal networks")
  Member "structure measure a new way to evaluate foreground maps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection")

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)