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: "learning attraction field representation for robust line segment detection"
  Abstract: This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three components: (i) A region-partition map in which every pixel is assigned to one and only one line segment; (ii) An attraction field map in which every pixel in a partition region is encoded by its 2D projection vector w.r.t. the associated line segment; and (iii) A squeeze module which squashes the attraction field to a line segment map that almost perfectly recovers the input one. By leveraging the duality, we learn ConvNets to compute the attraction field maps for raw in-put images, followed by the squeeze module for LSD, in an end-to-end manner. Our method rigorously addresses several challenges in LSD such as local ambiguity and class imbalance. Our method also harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution. In experiments, our method is tested on the WireFrame dataset and the YorkUrban dataset with state-of-the-art performance obtained. Especially, we advance the performance by 4.5 percents on the WireFrame dataset. Our method is also fast with 6.6~10.4 FPS, outperforming most of existing line segment detectors.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-2 (induced by source paper "holistically attracted wireframe parsing"):
  with members:
    - "learning regional attraction for line segment detection"
    - "learning to calibrate straight lines for fisheye image rectification"
    - "ppgnet learning point pair graph for line segment detection"
    - "surface reconstruction from 3d line segments"
    - "point based multi view stereo network"
    - "end to end wireframe parsing"
    - "plmp point line minimal problems in complete multi view visibility"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-3 (induced by source paper "mcmlsd a probabilistic algorithm and evaluation framework for line segment detection"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-4 (induced by source paper "learning structure appearance joint embedding for indoor scene image synthesis"):
  with members:
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "semantic image synthesis with spatially adaptive normalization"
    - "end to end wireframe parsing"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semi parametric image synthesis"

Connected through hyperedge HE-5 (induced by source paper "end to end wireframe parsing"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "cornernet detecting objects as paired keypoints"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "deformable convolutional networks"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "stacked hourglass networks for human pose estimation"
    - "fully convolutional instance aware semantic segmentation"

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"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "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 "sem lsd a learning based semantic line segment detector"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "ppgnet learning point pair graph for line segment detection"
    - "end to end wireframe parsing"
    - "cornernet detecting objects as paired keypoints"
    - "comparison of semantic segmentation approaches for horizon sky line detection"
    - "focal loss for dense object detection"
    - "semantic visual localization"
    - "casenet deep category aware semantic edge detection"

Connected through hyperedge HE-8 (induced by source paper "bags an automatic homework grading system using the pictures taken by smart phones"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "tensorflow a system for large scale machine learning"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

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)