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: "convolutional oriented boundaries"
  Abstract: We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and MS-COCO, showing that COB provides state-of-the-art contours, region hierarchies, and object proposals in all datasets.

Connected through hyperedge HE-1 (induced by source paper "instance segmentation with point supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "learning to segment via cut and paste"
    - "where are the blobs counting by localization with point supervision"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on the importance of visual context for data augmentation in scene understanding"

Connected through hyperedge HE-2 (induced by source paper "cali sketch stroke calibration and completion for high quality face image generation from poorly drawn sketches"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "main multi attention instance network for video segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "fast online object tracking and segmentation a unifying approach"
    - "youtube vos a large scale video object segmentation benchmark"
    - "video object segmentation with joint re identification and attention aware mask propagation"

Connected through hyperedge HE-4 (induced by source paper "towards partial supervision for generic object counting in natural scenes"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "learning spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "where are the masks instance segmentation with image level supervision"
    - "object counting and instance segmentation with image level supervision"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-5 (induced by source paper "where are the masks instance segmentation with image level supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "instance segmentation with point supervision"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "yolact real time instance segmentation"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-6 (induced by source paper "bi directional cascade network for perceptual edge detection"):
  with members:
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "multi scale dense networks for resource efficient image classification"
    - "srn side output residual network for object symmetry detection in the wild"
    - "hierarchical multi scale attention networks for action recognition"
    - "convolutional oriented boundaries from image segmentation to high level tasks"

Connected through hyperedge HE-7 (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"
    - "salient object subitizing"
    - "learning to refine object segments"

Connected through hyperedge HE-8 (induced by source paper "learning regional attraction for line segment detection"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"

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)