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: "a simple pooling based design for real time salient object detection"
  Abstract: We solve the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks. Based on the U-shape architecture, we first build a global guidance module (GGM) upon the bottom-up pathway, aiming at providing layers at different feature levels the location information of potential salient objects. We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway. By adding FAMs after the fusion operations in the top-down pathway, coarse-level features from the GGM can be seamlessly merged with features at various scales. These two pooling-based modules allow the high-level semantic features to be progressively refined, yielding detail enriched saliency maps. Experiment results show that our proposed approach can more accurately locate the salient objects with sharpened details and hence substantially improve the performance compared to the previous state-of-the-arts. Our approach is fast as well and can run at a speed of more than 30 FPS when processing a $300 \times 400$ image. Code can be found at this http URL.

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"
    - "egnet edge guidance network for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-3 (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"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "salient object detection a survey"
    - "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-4 (induced by source paper "abba saliency regularized motion based adversarial blur attack"):
  with members:
    - "effects of blur and deblurring to visual object tracking"
    - "brain inspired reverse adversarial examples"
    - "spatial aware online adversarial perturbations against visual object tracking"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "amora black box adversarial morphing attack"
    - "adversarial attacks and defences competition"
    - "learning to synthesize motion blur"
    - "adef an iterative algorithm to construct adversarial deformations"

Connected through hyperedge HE-5 (induced by source paper "global context aware progressive aggregation network for salient object detection"):
  with members:
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "review of visual saliency detection with comprehensive information"
    - "hscs hierarchical sparsity based co saliency detection for rgbd images"
    - "reverse attention for salient object detection"
    - "an iterative co saliency framework for rgbd images"
    - "structure measure a new way to evaluate foreground maps"
    - "picanet learning pixel wise contextual attention for saliency detection"

Connected through hyperedge HE-6 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "egnet edge guidance network for salient object detection"
    - "pdnet prior model guided depth enhanced network for salient object detection"
    - "rgbd salient object detection via deep fusion"
    - "non local neural networks"
    - "attention is all you need"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-7 (induced by source paper "a fixation based 360 benchmark dataset for salient object detection"):
  with members:
    - "global context aware progressive aggregation network for salient object detection"
    - "distortion adaptive salient object detection in 360 circ omnidirectional images"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"

Connected through hyperedge HE-8 (induced by source paper "res2net a new multi scale backbone architecture"):
  with members:
    - "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"
    - "salient object detection a survey"
    - "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"

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)