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: "predicting human eye fixations via an lstm based saliency attentive model"
  Abstract: Data-driven saliency has recently gained a lot of attention thanks to the use of convolutional neural networks for predicting gaze fixations. In this paper, we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a convolutional long short-term memory that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. In addition, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture outperforms the current state-of-the-art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios.

Connected through hyperedge HE-1 (induced by source paper "how much position information do convolutional neural networks encode"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "richer and deeper supervision network for salient object detection"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "eml net an expandable multi layer network for saliency prediction"
    - "dynamic routing between capsules"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "direction concentration learning enhancing congruency in machine learning"):
  with members:
    - "learning to detect"
    - "a dilated inception network for visual saliency prediction"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "natural gradient deep q learning"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "dual glance model for deciphering social relationships"
    - "gradient episodic memory for continual learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (induced by source paper "see attend and brake an attention based saliency map prediction model for end to end driving"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "predicting driver attention in critical situations"
    - "learning where to attend like a human driver"
    - "end to end learning for self driving cars"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

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"
    - "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"
    - "learning transferable architectures for scalable image recognition"

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"
    - "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"
    - "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"
    - "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"
    - "end to end saliency mapping via probability distribution prediction"

Connected through hyperedge HE-7 (induced by source paper "attention flow end to end joint attention estimation"):
  with members:
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "teacher s perception in the classroom"
    - "cbam convolutional block attention module"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "supervising neural attention models for video captioning by human gaze data"
    - "residual attention network for image classification"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-8 (induced by source paper "revisiting saliency metrics farthest neighbor area under curve"):
  with members:
    - "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"
    - "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)