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: "end to end saliency mapping via probability distribution prediction"
  Abstract: Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures. However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps. In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution. We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions. We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.

Connected through hyperedge HE-1 (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"
    - "susinet see understand and summarize it"
    - "the sound of pixels"

Connected through hyperedge HE-2 (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"
    - "saliency prediction in the deep learning era an empirical investigation"

Connected through hyperedge HE-3 (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"
    - "learning markov clustering networks for scene text detection"
    - "dilated residual networks"
    - "dual path networks"

Connected through hyperedge HE-4 (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"
    - "saliency benchmarking made easy separating models maps and metrics"
    - "dual path networks"
    - "saliency revisited analysis of mouse movements versus fixations"

Connected through hyperedge HE-5 (induced by source paper "what can computational models learn from human selective attention a review from an audiovisual crossmodal perspective"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "enhanced robot speech recognition using biomimetic binaural sound source localization"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "cross modal attentional context learning for rgb d object detection"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "assessing the contribution of semantic congruency to multisensory integration and conflict resolution"

Connected through hyperedge HE-6 (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"
    - "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-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"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "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 "light weighted saliency detection with distinctively lower memory cost and model size"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

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)