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: "saliency prediction in the deep learning era an empirical investigation"
  Abstract: Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans and discuss remaining issues that need to be addressed to build the next generation of more powerful saliency models. Some specific questions that are addressed include: in what ways current models fail, how to remedy them, what can be learned from cognitive studies of attention, how explicit saliency judgments relate to fixations, how to conduct fair model comparison, and what are the emerging applications of saliency models.

Connected through hyperedge HE-1 (induced by source paper "unified image and video saliency modeling"):
  with members:
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "tased net temporally aggregating spatial encoder decoder network for video saliency detection"
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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"
    - "end to end saliency mapping via probability distribution prediction"

Connected through hyperedge HE-2 (induced by source paper "toward improving the evaluation of visual attention models a crowdsourcing approach"):
  with members:
    - "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"
    - "cat2000 a large scale fixation dataset for boosting saliency research"

Connected through hyperedge HE-3 (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"
    - "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"
    - "saliency revisited analysis of mouse movements versus fixations"

Connected through hyperedge HE-4 (induced by source paper "deep saliency models the quest for the loss function"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "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-5 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "deepvs a deep learning based video saliency prediction approach"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-6 (induced by source paper "dave a deep audio visual embedding for dynamic saliency prediction"):
  with members:
    - "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"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "eml net an expandable multi layer network for saliency prediction"
    - "saliency benchmarking made easy separating models maps and metrics"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-7 (induced by source paper "understanding and visualizing deep visual saliency models"):
  with members:
    - "deep visual attention prediction"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "end to end saliency mapping via probability distribution prediction"
    - "network dissection quantifying interpretability of deep visual representations"
    - "saliency revisited analysis of mouse movements versus fixations"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "shallow and deep convolutional networks for saliency prediction"

Connected through hyperedge HE-8 (induced by source paper "human attention in image captioning dataset and analysis"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "boosted attention leveraging human attention for image captioning"
    - "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"

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)