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: "visual saliency based on multiscale deep features"
  Abstract: Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this CVPR 2015 paper, we discover that a high-quality visual saliency model can be trained with multiscale features extracted using a popular deep learning architecture, convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for extracting features at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotation. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-2 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised salient object detection via scribble annotations"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "gated crf loss for weakly supervised semantic image segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "weakly supervised salient object detection using image labels"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "cross layer feature pyramid network for salient object detection"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "reverse attention for salient object detection"

Connected through hyperedge HE-5 (induced by source paper "fast video object segmentation using the global context module"):
  with members:
    - "video object segmentation using space time memory networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "a generative appearance model for end to end video object segmentation"
    - "videomatch matching based video object segmentation"

Connected through hyperedge HE-6 (induced by source paper "tidying deep saliency prediction architectures"):
  with members:
    - "how is gaze influenced by image transformations dataset and model"
    - "contextual encoder decoder network for visual saliency prediction"
    - "deep visual attention prediction"
    - "influence of image classification accuracy on saliency map estimation"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "attention based active visual search for mobile robots"
    - "eml net an expandable multi layer network for saliency prediction"
    - "saliency benchmarking made easy separating models maps and metrics"

Connected through hyperedge HE-7 (induced by source paper "global context aware progressive aggregation network for salient object detection"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "review of visual saliency detection with comprehensive information"
    - "hscs hierarchical sparsity based co saliency detection for rgbd images"
    - "reverse attention for salient object detection"
    - "an iterative co saliency framework for rgbd images"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-8 (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"

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)