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: "towards the success rate of one real time unconstrained salient object detection"
  Abstract: In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks. Instead of generating thousands of candidate bounding boxes and refining them, our network directly learns to generate the saliency map containing the exact number of salient objects. During training, we convert the ground-truth rectangular boxes to Gaussian distributions that better capture the ROI regarding individual salient objects. During inference, the network predicts Gaussian distributions centered at salient objects with an appropriate covariance, from which bounding boxes are easily inferred. Notably, our network performs saliency map prediction without pixel-level annotations, salient object detection without object proposals, and salient object subitizing simultaneously, all in a single pass within a unified framework. Extensive experiments show that our approach outperforms existing methods on various datasets by a large margin, and achieves more than 100 fps with VGG16 network on a single GPU during inference.

Connected through hyperedge HE-1 (induced by source paper "detecting lesion bounding ellipses with gaussian proposal networks"):
  with members:
    - "bounding box regression with uncertainty for accurate object detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "mask r cnn"
    - "learning a rotation invariant detector with rotatable bounding box"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "autofocus efficient multi scale inference"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "optimizing video object detection via a scale time lattice"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "soft nms improving object detection with one line of code"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"

Connected through hyperedge HE-3 (induced by source paper "salient object detection in video using deep non local neural networks"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "video salient object detection using spatiotemporal deep features"
    - "review of visual saliency detection with comprehensive information"
    - "deepvs a deep learning based video saliency prediction approach"
    - "difnet semantic segmentation by diffusion networks"
    - "non local recurrent network for image restoration"

Connected through hyperedge HE-4 (induced by source paper "relative saliency and ranking models metrics data and benchmarks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep learning is robust to massive label noise"
    - "gated feedback refinement network for coarse to fine dense semantic image labeling"
    - "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"
    - "structure measure a new way to evaluate foreground maps"
    - "label refinement network for coarse to fine semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "label refinement network for coarse to fine semantic segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"

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)