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: "deep crisp boundaries from boundaries to higher level tasks"
  Abstract: Edge detection has made significant progress with the help of deep convolutional networks (ConvNet). These ConvNet-based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors’ outputs. We show that the detection results did not accurately localize edge pixels, which can be adversarial for tasks that require crisp edge inputs. As a remedy, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve superior performance, surpassing human accuracy when using standard criteria on BSDS500, and largely outperforming the state-of-the-art methods when using more strict criteria. More importantly, we demonstrate the benefit of crisp edge maps for several important applications in computer vision, including optical flow estimation, object proposal generation, and semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "object contour and edge detection with refinecontournet"):
  with members:
    - "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"
    - "object contour detection with a fully convolutional encoder decoder network"

Connected through hyperedge HE-2 (induced by source paper "ddnet dual path decoder network for occlusion relationship reasoning"):
  with members:
    - "context constrained accurate contour extraction for occlusion edge detection"
    - "bi directional cascade network for perceptual edge detection"
    - "occlusion shared and feature separated network for occlusion relationship reasoning"
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "doc deep occlusion estimation from a single image"
    - "holistically nested edge detection"

Connected through hyperedge HE-3 (induced by source paper "semantic edge detection with diverse deep supervision"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "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"
    - "learning to predict crisp boundaries"

Connected through hyperedge HE-4 (induced by source paper "richer convolutional features for edge detection"):
  with members:
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "webseg learning semantic segmentation from web searches"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "casenet deep category aware semantic edge detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder network"

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)