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: "saliency revisited analysis of mouse movements versus fixations"
  Abstract: This paper revisits visual saliency prediction by evaluating the recent advancements in this field such as crowd-sourced mouse tracking-based databases and contextual annotations. We pursue a critical and quantitative approach towards some of the new challenges including the quality of mouse tracking versus eye tracking for model training and evaluation. We extend quantitative evaluation of models in order to incorporate contextual information by proposing an evaluation methodology that allows accounting for contextual factors such as text, faces, and object attributes. The proposed contextual evaluation scheme facilitates detailed analysis of models and helps identify their pros and cons. Through several experiments, we find that (1) mouse tracking data has lower inter-participant visual congruency and higher dispersion, compared to the eye tracking data, (2) mouse tracking data does not totally agree with eye tracking in general and in terms of different contextual regions in specific, and (3) mouse tracking data leads to acceptable results in training current existing models, and (4) mouse tracking data is less reliable for model selection and evaluation. The contextual evaluation also reveals that, among the studied models, there is no single model that performs best on all the tested annotations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "contextual encoder decoder network for visual saliency prediction"):
  with members:
    - "influence of image classification accuracy on saliency map estimation"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "invariance analysis of saliency models versus human gaze during scene free viewing"
    - "eml net an expandable multi layer network for saliency prediction"
    - "end to end saliency mapping via probability distribution prediction"
    - "saliency benchmarking made easy separating models maps and metrics"
    - "dual path networks"

Connected through hyperedge HE-3 (induced by source paper "deep saliency models the quest for the loss function"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "eml net an expandable multi layer network for saliency prediction"
    - "an element sensitive saliency model with position prior learning for web pages"
    - "end to end saliency mapping via probability distribution prediction"
    - "video salient object detection via fully convolutional networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-4 (induced by source paper "dave a deep audio visual embedding for dynamic saliency prediction"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "eml net an expandable multi layer network for saliency prediction"
    - "saliency benchmarking made easy separating models maps and metrics"

Connected through hyperedge HE-5 (induced by source paper "understanding and visualizing deep visual saliency models"):
  with members:
    - "deep visual attention prediction"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "end to end saliency mapping via probability distribution prediction"
    - "network dissection quantifying interpretability of deep visual representations"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "shallow and deep convolutional networks for saliency prediction"

Connected through hyperedge HE-6 (induced by source paper "crowdfix an eyetracking dataset of real life crowd videos"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"

Connected through hyperedge HE-7 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"

Connected through hyperedge HE-8 (induced by source paper "bottom up attention models of"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "eml net an expandable multi layer network for saliency prediction"

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)