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: "opensalicon an open source implementation of the salicon saliency model"
  Abstract: In this technical report, we present our publicly downloadable implementation of the SALICON saliency model. At the time of this writing, SALICON is one of the top performing saliency models on the MIT 300 fixation prediction dataset which evaluates how well an algorithm is able to predict where humans would look in a given image. Recently, numerous models have achieved state-of-the-art performance on this benchmark, but none of the top 5 performing models (including SALICON) are available for download. To address this issue, we have created a publicly downloadable implementation of the SALICON model. It is our hope that our model will engender further research in visual attention modeling by providing a baseline for comparison of other algorithms and a platform for extending this implementation. The model we provide supports both training and testing, enabling researchers to quickly fine-tune the model on their own dataset. We also provide a pre-trained model and code for those users who only need to generate saliency maps for images without training their own model.

Connected through hyperedge HE-1 (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-2 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep visual attention prediction"
    - "feature space transfer for data augmentation"
    - "dynamic few shot visual learning without forgetting"
    - "deep layer aggregation"
    - "prototypical networks for few shot learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"

Connected through hyperedge HE-3 (induced by source paper "leverage eye movement data for saliency modeling invariance analysis and a robust new model"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "deep visual attention prediction"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "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"
    - "measuring neural net robustness with constraints"

Connected through hyperedge HE-4 (induced by source paper "sid4vam a benchmark dataset with synthetic images for visual attention modeling"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "psychophysical evaluation of individual low level feature influences on visual attention"
    - "the elephant in the room"
    - "a neurodynamic model of saliency prediction in v1"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "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-5 (induced by source paper "rapid visual categorization is not guided by early salience based selection"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "top down neural attention by excitation backprop"
    - "what do different evaluation metrics tell us about saliency models"
    - "salient object detection a benchmark"
    - "very deep convolutional networks for large scale image recognition"
    - "multiple object recognition with visual attention"

Connected through hyperedge HE-6 (induced by source paper "what catches the eye visualizing and understanding deep 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"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-7 (induced by source paper "semantic and contrast aware saliency"):
  with members:
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "what do different evaluation metrics tell us about saliency models"
    - "holistically nested edge detection"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-8 (induced by source paper "saliency benchmarking made easy separating models maps and metrics"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "end to end saliency mapping via probability distribution prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "computational models bottom up and top down aspects"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"

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)