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: "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
  Abstract: Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network. Our method can be viewed as a "High-for-Low" approach where high-level object features inform the low-level boundary detection process. Our model achieves state-of-the-art performance on an established boundary detection benchmark and it is efficient to run. #R##N#Additionally, we show that due to the semantic nature of our boundaries we can use them to aid a number of high-level vision tasks. We demonstrate that using our boundaries we improve the performance of state-of-the-art methods on the problems of semantic boundary labeling, semantic segmentation and object proposal generation. We can view this process as a "Low-for-High" scheme, where low-level boundaries aid high-level vision tasks. #R##N#Thus, our contributions include a boundary detection system that is accurate, efficient, generalizes well to multiple datasets, and is also shown to improve existing state-of-the-art high-level vision methods on three distinct tasks.

Connected through hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network"):
  with members:
    - "transductive few shot learning with meta learned confidence"
    - "dense classification and implanting for few shot learning"
    - "bi directional cascade network for perceptual edge detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "dynamic feature fusion for semantic edge detection"
    - "panet few shot image semantic segmentation with prototype alignment"

Connected through hyperedge HE-2 (induced by source paper "deep crisp boundaries from boundaries to higher level tasks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "understanding convolution for semantic segmentation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "object contour detection with a fully convolutional encoder decoder network"

Connected through hyperedge HE-3 (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-4 (induced by source paper "upi net semantic contour detection in placental ultrasound"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "bam bottleneck attention module"
    - "group normalization"
    - "attention guided unified network for panoptic segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-5 (induced by source paper "object contour and edge detection with refinecontournet"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "learning to predict crisp boundaries"
    - "boundary flow a siamese network that predicts boundary motion without training on motion"
    - "richer convolutional features for edge detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "dynamic feature fusion for semantic edge detection"):
  with members:
    - "semantic edge detection with diverse deep supervision"
    - "simultaneous edge alignment and learning"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "semantic edge detection with diverse deep supervision"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "dynamic feature fusion for semantic edge detection"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "taskonomy disentangling task transfer learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"

Connected through hyperedge HE-8 (induced by source paper "lego learning edge with geometry all at once by watching videos"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"

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)