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: "learning visual importance for graphic designs and data visualizations"
  Abstract: Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process.

Connected through hyperedge HE-1 (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "dvqa understanding data visualizations via question answering"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "understanding infographics through textual and visual tag prediction"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "turkeyes a web based toolbox for crowdsourcing attention data"):
  with members:
    - "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
    - "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-3 (induced by source paper "guicomp a gui design assistant with real time multi faceted feedback"):
  with members:
    - "automated reporting of gui design violations for mobile apps"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "a computational method for evaluating ui patterns"
    - "pix2code generating code from a graphical user interface screenshot"

Connected through hyperedge HE-4 (induced by source paper "neural design network graphic layout generation with constraints"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "generating diverse high fidelity images with vq vae 2"
    - "layoutvae stochastic scene layout generation from a label set"
    - "dancing to music"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "graph r cnn for scene graph generation"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "vignette perceptual compression for video storage and processing systems"):
  with members:
    - "scanner efficient video analysis at scale"
    - "a deep multi level network for saliency prediction"
    - "yolo9000 better faster stronger"
    - "fast video classification via adaptive cascading of deep models"

Connected through hyperedge HE-6 (induced by source paper "modeling mobile interface tappability using crowdsourcing and deep learning"):
  with members:
    - "predicting human performance in vertical menu selection using deep learning"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "synthetically trained icon proposals for parsing and summarizing infographics"):
  with members:
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "the amazing mysteries of the gutter drawing inferences between panels in comic book narratives"
    - "synthetic data for text localisation in natural images"
    - "salient object detection a benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "saliency prediction for mobile user interfaces"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep multi level network for saliency prediction"
    - "eye tracking for everyone"
    - "shallow and deep convolutional networks for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "adam a method for stochastic optimization"

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)