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 discriminative regional feature integration approach"
  Abstract: Feature integration provides a computational framework for saliency detection, and a lot of hand-crafted integration rules have been developed. In this paper, we present a principled extension, supervised feature integration, which learns a random forest regressor to discriminatively integrate the saliency features for saliency computation. In addition to contrast features, we introduce regional object-sensitive descriptors: the objectness descriptor characterizing the common spatial and appearance property of the salient object, and the image-specific backgroundness descriptor characterizing the appearance of the background of a specific image, which are shown more important for estimating the saliency. To the best of our knowledge, our supervised feature integration framework is the first successful approach to perform the integration over the saliency features for salient object detection, and outperforms the integration approach over the saliency maps. Together with fusing the multi-level regional saliency maps to impose the spatial saliency consistency, our approach significantly outperforms state-of-the-art methods on seven benchmark datasets. We also discuss several followup works which jointly learn the representation and the saliency map using deep learning.

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 "super diffusion for salient object detection"):
  with members:
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "hierarchical cellular automata for visual saliency"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "salient object detection a benchmark"

Connected through hyperedge HE-3 (induced by source paper "a multi criteria approach for fast and outlier aware representative selection from manifolds"):
  with members:
    - "dissimilarity based sparse subset selection"
    - "greedy column subset selection for large scale data sets"
    - "robust multiple manifolds structure learning"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"
    - "an improved approximation algorithm for the column subset selection problem"

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"
    - "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"

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"
    - "salient object detection a survey"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deeply supervised salient object detection with short connections"):
  with members:
    - "salient object detection a survey"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "structure measure a new way to evaluate foreground maps"
    - "richer convolutional features for edge detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "perceptual generative adversarial networks for small object detection"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"

Connected through hyperedge HE-7 (induced by source paper "data priming network for automatic check out"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "deeply supervised salient object detection with short connections"
    - "mvtec d2s densely segmented supermarket dataset"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "saliency integration an arbitrator model"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "hierarchical cellular automata for visual saliency"
    - "deep contrast learning for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "salient object detection a benchmark"
    - "fixation prediction with a combined model of bottom up saliency and vanishing point"
    - "a reverse hierarchy model for predicting eye fixations"
    - "visual saliency based on scale space analysis in the frequency domain"

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)