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 a benchmark"
  Abstract: We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.

Connected through hyperedge HE-1 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "few shot learning via saliency guided hallucination of samples"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "deeply supervised salient object detection with short connections"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"

Connected through hyperedge HE-2 (induced by source paper "deep green function convolution for improving saliency in convolutional neural networks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "richer convolutional features for edge detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "recurrent attentional networks for saliency detection"

Connected through hyperedge HE-3 (induced by source paper "saliency enhancement using gradient domain edges merging"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "novel convolution kernels for computer vision and shape analysis based on electromagnetism"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-4 (induced by source paper "super diffusion for salient object detection"):
  with members:
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "hierarchical cellular automata for visual saliency"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"

Connected through hyperedge HE-5 (induced by source paper "res2net a new multi scale backbone architecture"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "high frequency residual learning for multi scale image classification"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "human vs machine attention in neural networks a comparative study"):
  with members:
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "reciprocal attention fusion for visual question answering"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "fine grained video categorization with redundancy reduction attention"
    - "reverse attention for salient object detection"
    - "learn to pay attention"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-7 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "richer and deeper supervision network for salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"
    - "deep visual attention prediction"

Connected through hyperedge HE-8 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"

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)