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: "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
  Abstract: In this article, we present BubbleView, an alternative methodology for eye tracking using discrete mouse clicks to measure which information people consciously choose to examine. BubbleView is a mouse-contingent, moving-window interface in which participants are presented with a series of blurred images and click to reveal “bubbles” -- small, circular areas of the image at original resolution, similar to having a confined area of focus like the eye fovea. Across 10 experiments with 28 different parameter combinations, we evaluated BubbleView on a variety of image types: information visualizations, natural images, static webpages, and graphic designs, and compared the clicks to eye fixations collected with eye-trackers in controlled lab settings. We found that BubbleView clicks can both (i) successfully approximate eye fixations on different images, and (ii) be used to rank image and design elements by importance. BubbleView is designed to collect clicks on static images, and works best for defined tasks such as describing the content of an information visualization or measuring image importance. BubbleView data is cleaner and more consistent than related methodologies that use continuous mouse movements. Our analyses validate the use of mouse-contingent, moving-window methodologies as approximating eye fixations for different image and task types.

Connected through hyperedge HE-1 (induced by source paper "turkeyes a web based toolbox for crowdsourcing attention data"):
  with members:
    - "learning visual importance for graphic designs and data visualizations"
    - "eye tracking for everyone"
    - "what do different evaluation metrics tell us about saliency models"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "crowdsourcing gaze data collection"

Connected through hyperedge HE-2 (induced by source paper "attention based glaucoma detection a large scale database and cnn model"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "supervising neural attention models for video captioning by human gaze data"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "predicting video saliency using crowdsourced mouse tracking data"):
  with members:
    - "improving video compression with deep visual attention models"
    - "are all the frames equally important"
    - "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"
    - "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"

Connected through hyperedge HE-4 (induced by source paper "are all the frames equally important"):
  with members:
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "deepfix a fully convolutional neural network for predicting human eye 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:
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning visual importance for graphic designs and data visualizations"):
  with members:
    - "saliency benchmarking separating models maps and metrics"
    - "a deep multi level network for saliency prediction"
    - "eye tracking for everyone"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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.HC(Human-Computer Interaction)