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: "visual saliency detection based on multiscale deep cnn features"
  Abstract: Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for the manipulation of eye tracking data"):
  with members:
    - "differential privacy for eye tracking data"
    - "privaceye privacy preserving head mounted eye tracking using egocentric scene image and eye movement features"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "rainbow combining improvements in deep reinforcement learning"
    - "knock knock who s there membership inference on aggregate location data"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"
    - "salnet360 saliency maps for omni directional images with cnn"

Connected through hyperedge HE-4 (induced by source paper "rosa robust salient object detection against adversarial attacks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "contrast oriented deep neural networks for salient object detection"
    - "weakly supervised salient object detection using image labels"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "reverse attention for salient object detection"
    - "hierarchical cellular automata for visual saliency"

Connected through hyperedge HE-5 (induced by source paper "cross modal relationship inference for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "contrast oriented deep neural networks for salient object detection"
    - "mattnet modular attention network for referring expression comprehension"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "exploring visual relationship for image captioning"
    - "context encoding for semantic segmentation"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-6 (induced by source paper "salient object detection with purificatory mechanism and structural similarity loss"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "selectivity or invariance boundary aware salient object detection"
    - "reverse attention for salient object detection"
    - "dual attention network for scene segmentation"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-7 (induced by source paper "cross modal attentional context learning for rgb d object detection"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "instance level salient object segmentation"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deep feature based contextual model for object detection"
    - "ssd single shot multibox detector"
    - "human pose estimation from depth images via inference embedded multi task learning"
    - "attentive contexts for object detection"

Connected through hyperedge HE-8 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "salient object subitizing"

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)