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 scale space analysis in the frequency domain"
  Abstract: We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of nonsaliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"

Connected through hyperedge HE-2 (induced by source paper "salient object detection a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "instance level salient object segmentation"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "what makes for effective detection proposals"

Connected through hyperedge HE-3 (induced by source paper "ultrafast video attention prediction with coupled knowledge distillation"):
  with members:
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepvs a deep learning based video saliency prediction approach"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "cbam convolutional block attention module"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "spatio temporal saliency networks for dynamic saliency prediction"

Connected through hyperedge HE-4 (induced by source paper "few shot abstract visual reasoning with spectral features"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "not so clevr visual relations strain feedforward neural networks"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "25 years of cnns can we compare to human abstraction capabilities"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

Connected through hyperedge HE-5 (induced by source paper "sid4vam a benchmark dataset with synthetic images for visual attention modeling"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "psychophysical evaluation of individual low level feature influences on visual attention"
    - "the elephant in the room"
    - "a neurodynamic model of saliency prediction in v1"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep multi level network for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "opensalicon an open source implementation of the salicon saliency model"

Connected through hyperedge HE-6 (induced by source paper "saliency integration an arbitrator model"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "hierarchical cellular automata for visual saliency"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep contrast learning for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "salient object detection a benchmark"
    - "fixation prediction with a combined model of bottom up saliency and vanishing point"
    - "a reverse hierarchy model for predicting eye fixations"

Connected through hyperedge HE-7 (induced by source paper "saliency detection based on structural dissimilarity induced by image quality assessment model"):
  with members:
    - "deep visual attention prediction"
    - "what catches the eye visualizing and understanding deep saliency models"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "visual attention on the sun what do existing models actually predict"):
  with members:
    - "deep visual attention prediction"
    - "end to end saliency mapping via probability distribution prediction"
    - "shallow and deep convolutional networks for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "visual saliency based on multiscale deep features"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"
    - "the secrets of salient object 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)