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: "salient object detection a survey"
  Abstract: Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.

Connected through hyperedge HE-1 (induced by source paper "cross layer feature pyramid network for salient object detection"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-2 (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"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "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-3 (induced by source paper "saliency enhancement using gradient domain edges merging"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "novel convolution kernels for computer vision and shape analysis based on electromagnetism"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-4 (induced by source paper "res2net a new multi scale backbone architecture"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "high frequency residual learning for multi scale image classification"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "deeply supervised salient object detection with short connections"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "simple baselines for human pose estimation and tracking"
    - "hi fi hierarchical feature integration for skeleton detection"

Connected through hyperedge HE-5 (induced by source paper "region refinement network for salient object detection"):
  with members:
    - "se2net siamese edge enhancement network for salient object detection"
    - "a simple pooling based design for real time salient object detection"
    - "dna deeply supervised nonlinear aggregation for salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "selectivity or invariance boundary aware salient object detection"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "salient objects in clutter bringing salient object detection to the foreground"

Connected through hyperedge HE-6 (induced by source paper "looking at the right stuff guided semantic gaze for autonomous driving"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "attention based dropout layer for weakly supervised object localization"
    - "dynamic traffic scene classification with space time coherence"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"

Connected through hyperedge HE-7 (induced by source paper "improved person re identification based on saliency and semantic parsing with deep neural network models"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "video salient object detection via fully convolutional networks"
    - "human semantic parsing for person re identification"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-8 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "salient object detection in the deep learning era an in depth survey"
    - "object part attention model for fine grained image classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "deep descriptor transforming for image co localization"

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)