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: "saliency benchmarking made easy separating models maps and metrics"
  Abstract: Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.

Connected through hyperedge HE-1 (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"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"

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"
    - "end to end saliency mapping via probability distribution prediction"
    - "dual path networks"
    - "saliency revisited analysis of mouse movements versus fixations"

Connected through hyperedge HE-5 (induced by source paper "dave a deep audio visual embedding for dynamic saliency prediction"):
  with members:
    - "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"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "eml net an expandable multi layer network for saliency prediction"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-6 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"

Connected through hyperedge HE-7 (induced by source paper "bottom up attention models of"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "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"
    - "eml net an expandable multi layer network for saliency prediction"

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)