human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "salient object detection via augmented hypotheses"
  Abstract: In this paper, we propose using \textit{augmented hypotheses} which consider objectness, foreground and compactness for salient object detection. Our algorithm consists of four basic steps. First, our method generates the objectness map via objectness hypotheses. Based on the objectness map, we estimate the foreground margin and compute the corresponding foreground map which prefers the foreground objects. From the objectness map and the foreground map, the compactness map is formed to favor the compact objects. We then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore- and background. We finally evaluate the proposed framework on two challenging datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that our method outperforms state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "deep discrete hashing with self supervised pairwise labels"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "hashnet deep learning to hash by continuation"
    - "resnet in resnet generalizing residual architectures"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a survey on learning to hash"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "learning to hash with binary deep neural network"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"):
  with members:
    - "bayesian joint modelling for object localisation in weakly labelled images"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "on learning to localize objects with minimal supervision"
    - "weakly supervised discovery of visual pattern configurations"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "novel evaluation metrics for seam carving based image retargeting"):
  with members:
    - "salient object detection with semantic priors"
    - "salient object detection a benchmark"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-4 (induced by source paper "salient object detection with semantic priors"):
  with members:
    - "visual saliency based on multiscale deep features"
    - "adaptive nonparametric image parsing"
    - "fully convolutional networks for semantic segmentation"

Question: Please tell me which class the center node 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)