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: "understanding and visualizing deep visual saliency models"
  Abstract: Recently, data-driven deep saliency models have achieved high performance and have outperformed classical saliency models, as demonstrated by results on datasets such as the MIT300 and SALICON. Yet, there remains a large gap between the performance of these models and the inter-human baseline. Some outstanding questions include what have these models learned, how and where they fail, and how they can be improved. This article attempts to answer these questions by analyzing the representations learned by individual neurons located at the intermediate layers of deep saliency models. To this end, we follow the steps of existing deep saliency models, that is borrowing a pre-trained model of object recognition to encode the visual features and learning a decoder to infer the saliency. We consider two cases when the encoder is used as a fixed feature extractor and when it is fine-tuned, and compare the inner representations of the network. To study how the learned representations depend on the task, we fine-tune the same network using the same image set but for two different tasks: saliency prediction versus scene classification. Our analyses reveal that: 1) some visual regions (e.g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e.g. head) over some others (e.g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e.g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data.

Connected through hyperedge HE-1 (induced by source paper "deep saliency models the quest for the loss function"):
  with members:
    - "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"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "human attention in image captioning dataset and analysis"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "crowdfix an eyetracking data set of human crowd video"):
  with members:
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "fixation prediction with a combined model of bottom up saliency and vanishing point"
    - "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"

Connected through hyperedge HE-4 (induced by source paper "crowdfix an eyetracking dataset of real life crowd videos"):
  with members:
    - "saliency revisited analysis of mouse movements versus fixations"
    - "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"

Connected through hyperedge HE-5 (induced by source paper "bottom up attention models of"):
  with members:
    - "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"
    - "saliency benchmarking made easy separating models maps and metrics"

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)