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: "the secrets of salient object segmentation"
  Abstract: In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient object benchmarks, called the dataset design bias, by over emphasizing the stereotypical concepts of saliency. The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing. Based on our analysis, we propose a new high quality dataset that offers both fixation and salient object segmentation ground-truth. With fixations and salient object being presented simultaneously, we are able to bridge the gap between fixations and salient objects, and propose a novel method for salient object segmentation. Finally, we report significant benchmark progress on three existing datasets of segmenting salient objects

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"
    - "a simple pooling based design for real time salient object detection"
    - "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"

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"
    - "a simple pooling based design for real time salient object 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"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised salient object detection via scribble annotations"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "gated crf loss for weakly supervised semantic image segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "weakly supervised salient object detection using image labels"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "dual convolutional lstm network for referring image segmentation"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "mattnet modular attention network for referring expression comprehension"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "recurrent multimodal interaction for referring image segmentation"
    - "mask r cnn"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-6 (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"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "global context aware progressive aggregation network for salient object detection"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "deep green function convolution for improving saliency in convolutional neural networks"):
  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"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "richer convolutional features for edge detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "recurrent attentional networks for saliency 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)