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: "cat2000 a large scale fixation dataset for boosting saliency research"
  Abstract: Saliency modeling has been an active research area in computer vision for about two decades. Existing state of the art models perform very well in predicting where people look in natural scenes. There is, however, the risk that these models may have been overfitting themselves to available small scale biased datasets, thus trapping the progress in a local minimum. To gain a deeper insight regarding current issues in saliency modeling and to better gauge progress, we recorded eye movements of 120 observers while they freely viewed a large number of naturalistic and artificial images. Our stimuli includes 4000 images; 200 from each of 20 categories covering different types of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor, Outdoor, Jumbled, Random, and Line drawings. We analyze some basic properties of this dataset and compare some successful models. We believe that our dataset opens new challenges for the next generation of saliency models and helps conduct behavioral studies on bottom-up visual attention.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"

Connected through hyperedge HE-2 (induced by source paper "see attend and brake an attention based saliency map prediction model for end to end driving"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "predicting driver attention in critical situations"
    - "learning where to attend like a human driver"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "how is gaze influenced by image transformations dataset and model"):
  with members:
    - "deep visual attention prediction"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "residual attention network for image classification"
    - "a deep multi level network for saliency prediction"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-4 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality"):
  with members:
    - "ritnet real time semantic segmentation of the eye for gaze tracking"
    - "appearance based 3d gaze estimation with personal calibration"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "unsupervised learning of depth and ego motion from video"
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-5 (induced by source paper "revisiting saliency metrics farthest neighbor area under curve"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "saliency benchmarking made easy separating models maps and metrics"
    - "residual attention network for image classification"
    - "a deep multi level network for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"

Connected through hyperedge HE-6 (induced by source paper "turkeyes a web based toolbox for crowdsourcing attention data"):
  with members:
    - "learning visual importance for graphic designs and data visualizations"
    - "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"
    - "crowdsourcing gaze data collection"

Connected through hyperedge HE-7 (induced by source paper "toward improving the evaluation of visual attention models a crowdsourcing approach"):
  with members:
    - "saliency prediction in the deep learning era an empirical investigation"
    - "fixatons a collection of human fixations datasets and metrics for scanpath similarity"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-8 (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"

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)