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: "gated feedback refinement network for coarse to fine dense semantic image labeling"
  Abstract: Effective integration of local and global contextual information is crucial for semantic segmentation and dense image labeling. We develop two encoder-decoder based deep learning architectures to address this problem. We first propose a network architecture called Label Refinement Network (LRN) that predicts segmentation labels in a coarse-to-fine fashion at several spatial resolutions. In this network, we also define loss functions at several stages to provide supervision at different stages of training. However, there are limits to the quality of refinement possible if ambiguous information is passed forward. In order to address this issue, we also propose Gated Feedback Refinement Network (G-FRNet) that addresses this limitation. Initially, G-FRNet makes a coarse-grained prediction which it progressively refines to recover details by effectively integrating local and global contextual information during the refinement stages. This is achieved by gate units proposed in this work, that control information passed forward in order to resolve the ambiguity. Experiments were conducted on four challenging dense labeling datasets (CamVid, PASCAL VOC 2012, Horse-Cow Parsing, PASCAL-Person-Part, and SUN-RGBD). G-FRNet achieves state-of-the-art semantic segmentation results on the CamVid and Horse-Cow Parsing datasets and produces results competitive with the best performing approaches that appear in the literature for the other three datasets.

Connected through hyperedge HE-1 (induced by source paper "efficient ladder style densenets for semantic segmentation of large images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "on the convergence of adam and beyond"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tree structured kronecker convolutional network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "recurrent iterative gating networks for semantic segmentation"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"
    - "label refinement network for coarse to fine semantic segmentation"
    - "recurrent human pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "geometric scene parsing with hierarchical lstm"
    - "residual networks behave like ensembles of relatively shallow networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "towards the success rate of one real time unconstrained salient object detection"
    - "structure measure a new way to evaluate foreground maps"
    - "label refinement network for coarse to fine semantic segmentation"

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)