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: "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
  Abstract: In this paper, we aim at solving pixel-wise binary problems, including salient object segmentation, skeleton extraction, and edge detection, by introducing a unified architecture. Previous works have proposed tailored methods for solving each of the three tasks independently. Here, we show that these tasks share some similarities that can be exploited for developing a unified framework. In particular, we introduce a horizontal cascade, each component of which is densely connected to the outputs of previous component. Stringing these components together allows us to effectively exploit features across different levels hierarchically to effectively address the multiple pixel-wise binary regression tasks. To assess the performance of our proposed network on these tasks, we carry out exhaustive evaluations on multiple representative datasets. Although these tasks are inherently very different, we show that our unified approach performs very well on all of them and works far better than current single-purpose state-of-the-art methods. All the code in this paper will be publicly available.

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-2 (induced by source paper "deep green function convolution for improving saliency in convolutional neural networks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "richer convolutional features for edge detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "recurrent attentional networks for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-3 (induced by source paper "saliency enhancement using gradient domain edges merging"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "novel convolution kernels for computer vision and shape analysis based on electromagnetism"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-4 (induced by source paper "improving convolutional neural networks via conservative field regularisation and integration"):
  with members:
    - "deep green function convolution for improving saliency in convolutional neural networks"
    - "deeply supervised salient object detection with short connections"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a simple pooling based design for real time salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "richer convolutional features for edge detection"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-6 (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"
    - "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"
    - "convolutional oriented boundaries"

Connected through hyperedge HE-7 (induced by source paper "skeletonnet shape pixel to skeleton pixel"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "object skeleton extraction in natural images by fusing scale associated deep side outputs"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "hypercolumns for object segmentation and fine grained localization"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-8 (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"
    - "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"

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)