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: "contrast oriented deep neural networks for salient object detection"
  Abstract: Deep convolutional neural networks have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patch-wise (region-wise) training and inference or fully convolutional networks. Methods in the former category are generally time-consuming due to severe storage and computational redundancies among overlapping patches. To overcome this deficiency, methods in the second category attempt to directly map a raw input image to a predicted dense saliency map in a single network forward pass. Though being very efficient, it is arduous for these methods to detect salient objects of different scales or salient regions with weak semantic information. In this paper, we develop hybrid contrast-oriented deep neural networks to overcome the aforementioned limitations. Each of our deep networks is composed of two complementary components, including a fully convolutional stream for dense prediction and a segment-level spatial pooling stream for sparse saliency inference. We further propose an attentional module that learns weight maps for fusing the two saliency predictions from these two streams. A tailored alternate scheme is designed to train these deep networks by fine-tuning pre-trained baseline models. Finally, a customized fully connected CRF model incorporating a salient contour feature embedding can be optionally applied as a post-processing step to improve spatial coherence and contour positioning in the fused result from these two streams. Extensive experiments on six benchmark datasets demonstrate that our proposed model can significantly outperform the state of the art in terms of all popular evaluation metrics.

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"
    - "mattnet modular attention network for referring expression comprehension"

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"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-3 (induced by source paper "does time delay feedback matter to small target motion detection against complex dynamic environments"):
  with members:
    - "a directionally selective small target motion detecting visual neural network in cluttered backgrounds"
    - "a robust visual system for small target motion detection against cluttered moving backgrounds"
    - "a feedback neural network for small target motion detection in cluttered backgrounds"
    - "perceptual generative adversarial networks for small object detection"
    - "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
    - "human pose estimation with iterative error feedback"

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"
    - "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"
    - "learning uncertain convolutional features for accurate saliency detection"

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"
    - "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"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-6 (induced by source paper "cross modal attentional context learning for rgb d object detection"):
  with members:
    - "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"
    - "visual saliency detection based on multiscale deep cnn features"

Connected through hyperedge HE-7 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "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"
    - "learning to refine object segments"

Connected through hyperedge HE-8 (induced by source paper "non local context encoder robust biomedical image segmentation against adversarial attacks"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "context encoding for semantic segmentation"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "accurate lung segmentation via network wise training of convolutional networks"
    - "instance level salient object segmentation"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

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)