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 "region refinement network for salient object detection"):
  Abstract: Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN), which recurrently filters redundant information and explicitly models boundary information for saliency detection. Different from existing refinement methods, we propose a Region Refinement Module (RRM) that optimizes salient region prediction by incorporating supervised attention masks in the intermediate refinement stages. The module only brings a minor increase in model size and yet significantly reduces false predictions from the background. To further refine boundary areas, we propose a Boundary Refinement Loss (BRL) that adds extra supervision for better distinguishing foreground from background. BRL is parameter free and easy to train. We further observe that BRL helps retain the integrity in prediction by refining the boundary. Extensive experiments on saliency detection datasets show that our refinement module and loss bring significant improvement to the baseline and can be easily applied to different frameworks. We also demonstrate that our proposed model generalizes well to portrait segmentation and shadow detection tasks.

Includes members:
  Member "se2net siamese edge enhancement network for salient object detection":
    also connected through other hyperedges:
      (none)
  Member "a simple pooling based design for real time salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "dna deeply supervised nonlinear aggregation for salient object detection":
    also connected through other hyperedges:
      (none)
  Member "cascaded partial decoder for fast and accurate salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "salient object detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross layer feature pyramid network for salient object detection")
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "selectivity or invariance boundary aware salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection")
  Member "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross layer feature pyramid network for salient object 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)