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: "salgan visual saliency prediction with generative adversarial networks"
  Abstract: We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE. Our results can be reproduced with the source code and trained models available at https://imatge-upc.github. io/saliency-salgan-2017/.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification"):
  with members:
    - "global second order pooling convolutional networks"
    - "yolov3 an incremental improvement"
    - "learning to navigate for fine grained classification"
    - "focal loss for dense object detection"
    - "improved bilinear pooling with cnns"
    - "diversified visual attention networks for fine grained object classification"
    - "low rank bilinear pooling for fine grained classification"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"

Connected through hyperedge HE-3 (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-4 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network"):
  with members:
    - "reppoints point set representation for object detection"
    - "simple vs complex temporal recurrences for video saliency prediction"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "tdan temporally deformable alignment network for video super resolution"

Connected through hyperedge HE-5 (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"
    - "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"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "seqgan sequence generative adversarial nets with policy gradient"

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)