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: "deep edge aware saliency detection"
  Abstract: There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performance for scenes with complex compositions, multiple salient objects, and salient objects of diverse scales. In particular, output maps of the existing methods remain low in spatial resolution causing blurred edges due to the stride and pooling operations, networks often neglect descriptive statistical and handcrafted priors that have potential to complement saliency detection results, and deep features at different layers stay mainly desolate waiting to be effectively fused to handle multi-scale salient objects. In this paper, we tackle these issues by a new fully convolutional neural network that jointly learns salient edges and saliency labels in an end-to-end fashion. Our framework first employs convolutional layers that reformulate the detection task as a dense labeling problem, then integrates handcrafted saliency features in a hierarchical manner into lower and higher levels of the deep network to leverage available information for multi-scale response, and finally refines the saliency map through dilated convolutions by imposing context. In this way, the salient edge priors are efficiently incorporated and the output resolution is significantly improved while keeping the memory requirements low, leading to cleaner and sharper object boundaries. Extensive experimental analyses on ten benchmarks demonstrate that our framework achieves consistently superior performance and attains robustness for complex scenes in comparison to the very recent state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "egnet edge guidance network for salient object detection"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "richer and deeper supervision network for salient object detection"
    - "ranet ranking attention network for fast video object segmentation"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"
    - "boundary guided feature aggregation network for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "edge guided non local fully convolutional network for salient object detection"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "edge preserving and multi scale contextual neural network for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "visual tracking via dynamic graph learning"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"

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)