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: "turkergaze crowdsourcing saliency with webcam based eye tracking"
  Abstract: Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow. Therefore, existing saliency prediction datasets are order-of-magnitudes smaller than typical datasets for other vision recognition tasks. The small size of these datasets limits the potential for training data intensive algorithms, and causes overfitting in benchmark evaluation. To address this deficiency, this paper introduces a webcam-based gaze tracking system that supports large-scale, crowdsourced eye tracking deployed on Amazon Mechanical Turk (AMTurk). By a combination of careful algorithm and gaming protocol design, our system obtains eye tracking data for saliency prediction comparable to data gathered in a traditional lab setting, with relatively lower cost and less effort on the part of the researchers. Using this tool, we build a saliency dataset for a large number of natural images. We will open-source our tool and provide a web server where researchers can upload their images to get eye tracking results from AMTurk.

Connected through hyperedge HE-1 (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-2 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-3 (induced by source paper "detecting out of distribution inputs in deep neural networks using an early layer output"):
  with members:
    - "learning deep features for one class classification"
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-4 (induced by source paper "unsupervised out of distribution detection by maximum classifier discrepancy"):
  with members:
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the extreme value machine"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "generative openmax for multi class open set classification"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "gaze360 physically unconstrained gaze estimation in the wild"):
  with members:
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "densepose dense human pose estimation in the wild"
    - "adversarial discriminative domain adaptation"
    - "temporal relational reasoning in videos"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"
    - "eye tracking for everyone"
    - "seeing with humans gaze assisted neural image captioning"

Connected through hyperedge HE-6 (induced by source paper "entity recognition at first sight improving ner with eye movement information"):
  with members:
    - "leveraging cognitive features for sentiment analysis"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "natural language processing almost from scratch"

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 "pathgan visual scanpath prediction with generative adversarial networks"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "a deep multi level network for saliency prediction"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "eye tracking for everyone"
    - "energy based generative adversarial network"
    - "generative image modeling using style and structure adversarial networks"

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)