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: "disc deep image saliency computing via progressive representation learning"
  Abstract: Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. Inspired by the effectiveness of recently developed feature learning, we provide a novel deep image saliency computing (DISC) framework for fine-grained image saliency computing. In particular, we model the image saliency from both the coarse-and fine-level observations, and utilize the deep convolutional neural network (CNN) to learn the saliency representation in a progressive manner. In particular, our saliency model is built upon two stacked CNNs. The first CNN generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. Furthermore, we integrate superpixel-based local context information in the first CNN to refine the coarse-level saliency map. Guided by the coarse saliency map, the second CNN focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. For a testing image, the two CNNs collaboratively conduct the saliency computing in one shot. Our DISC framework is capable of uniformly highlighting the objects of interest from complex background while preserving well object details. Extensive experiments on several standard benchmarks suggest that DISC outperforms other state-of-the-art methods and it also generalizes well across data sets without additional training. The executable version of DISC is available online:  http://vision.sysu.edu.cn/projects/DISC .

Connected through hyperedge HE-1 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "richer and deeper supervision network for salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"
    - "deep visual attention prediction"

Connected through hyperedge HE-2 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-3 (induced by source paper "salient object detection a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "instance level salient object segmentation"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "what makes for effective detection proposals"
    - "deepsaliency multi task deep neural network model for salient object detection"

Connected through hyperedge HE-4 (induced by source paper "contextualized spatial temporal network for taxi origin destination demand prediction"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "contrast oriented deep neural networks for salient object detection"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "non local neural networks"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "a long short term memory recurrent neural network framework for network traffic matrix prediction"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"

Connected through hyperedge HE-5 (induced by source paper "hierarchical annotation of images with two alternative forced choice metric learning"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "deep metric learning with hierarchical triplet loss"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "compact bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "three birds one stone a general architecture for salient object segmentation edge detection and skeleton extraction"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "rgbd salient object detection via deep fusion"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"

Connected through hyperedge HE-7 (induced by source paper "reverse attention for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "learning to refine object contours with a top down fully convolutional encoder decoder network"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "squeeze and excitation networks"
    - "srn side output residual network for object symmetry detection in the wild"
    - "instance level salient object segmentation"

Connected through hyperedge HE-8 (induced by source paper "neural task planning with and or graph representations"):
  with members:
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "learning model based planning from scratch"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "diversified visual attention networks for fine grained object classification"
    - "visual semantic planning using deep successor representations"
    - "cognitive mapping and planning for visual navigation"

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)