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: "deep gaze i boosting saliency prediction with feature maps trained on imagenet"
  Abstract: Recent results suggest that state-of-the-art saliency models perform far from optimal in predicting fixations. This lack in performance has been attributed to an inability to model the influence of high-level image features such as objects. Recent seminal advances in applying deep neural networks to tasks like object recognition suggests that they are able to capture this kind of structure. However, the enormous amount of training data necessary to train these networks makes them difficult to apply directly to saliency prediction. We present a novel way of reusing existing neural networks that have been pretrained on the task of object recognition in models of fixation prediction. Using the well-known network of Krizhevsky et al. (2012), we come up with a new saliency model that significantly outperforms all state-of-the-art models on the MIT Saliency Benchmark. We show that the structure of this network allows new insights in the psychophysics of fixation selection and potentially their neural implementation. To train our network, we build on recent work on the modeling of saliency as point processes.

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 "stavis spatio temporal audiovisual saliency network"):
  with members:
    - "dave a deep audio visual embedding for dynamic saliency prediction"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "audio visual event localization in unconstrained videos"
    - "end to end saliency mapping via probability distribution prediction"
    - "susinet see understand and summarize it"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for the manipulation of eye tracking data"):
  with members:
    - "differential privacy for eye tracking data"
    - "privaceye privacy preserving head mounted eye tracking using egocentric scene image and eye movement features"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "rainbow combining improvements in deep reinforcement learning"
    - "knock knock who s there membership inference on aggregate location data"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"
    - "salnet360 saliency maps for omni directional images with cnn"

Connected through hyperedge HE-4 (induced by source paper "tidying deep saliency prediction architectures"):
  with members:
    - "how is gaze influenced by image transformations dataset and model"
    - "contextual encoder decoder network for visual saliency prediction"
    - "deep visual attention prediction"
    - "influence of image classification accuracy on saliency map estimation"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "attention based active visual search for mobile robots"
    - "eml net an expandable multi layer network for saliency prediction"
    - "saliency benchmarking made easy separating models maps and metrics"

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"
    - "cat2000 a large scale fixation dataset for boosting saliency research"

Connected through hyperedge HE-6 (induced by source paper "a dilated inception network for visual saliency prediction"):
  with members:
    - "salient object detection a survey"
    - "deep visual attention prediction"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "what catches the eye visualizing and understanding deep saliency models"
    - "end to end saliency mapping via probability distribution prediction"
    - "learning markov clustering networks for scene text detection"
    - "dilated residual networks"

Connected through hyperedge HE-7 (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-8 (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"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "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"

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)