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: "end to end wireframe parsing"
  Abstract: We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and then extract straight lines with heuristic algorithms, our method is end-to-end trainable and can directly output a vectorized wireframe that contains semantically meaningful and geometrically salient junctions and lines. To better understand the quality of the outputs, we propose a new metric for wireframe evaluation that penalizes overlapped line segments and incorrect line connectivities. We conduct extensive experiments and show that our method significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms. We hope our simple approach can be served as a baseline for future wireframe parsing studies. Code has been made publicly available at this https URL.

Connected through hyperedge HE-1 (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"
    - "plmp point line minimal problems in complete multi view visibility"
    - "cornernet detecting objects as paired keypoints"
    - "learning attraction field representation for robust line segment detection"

Connected through hyperedge HE-2 (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"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "learning attraction field representation for robust line segment detection"
    - "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-3 (induced by source paper "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "ppgnet learning point pair graph for line segment detection"
    - "deep high resolution representation learning for human pose estimation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spacenet a remote sensing dataset and challenge series"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "unsupervised image to sequence translation with canvas drawer networks"

Connected through hyperedge HE-4 (induced by source paper "neurvps neural vanishing point scanning via conic convolution"):
  with members:
    - "spherical cnns on unstructured grids"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "active convolution learning the shape of convolution for image classification"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "deformable convolutional networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"

Connected through hyperedge HE-5 (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"
    - "cornernet detecting objects as paired keypoints"
    - "learning attraction field representation for robust line segment detection"
    - "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-6 (induced by source paper "line as object datasets and framework for semantic line segment detection"):
  with members:
    - "ppgnet learning point pair graph for line segment detection"
    - "cornernet detecting objects as paired keypoints"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "focal loss for dense object detection"
    - "deep layer aggregation"
    - "deformable convolutional networks"
    - "towards accurate multi person pose estimation in the wild"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "vectorizing world buildings planar graph reconstruction by primitive detection and relationship inference"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "darnet deep active ray network for building segmentation"
    - "ppgnet learning point pair graph for line segment detection"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "dilated residual networks"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-8 (induced by source paper "vectorizing world buildings planar graph reconstruction by primitive detection and relationship classification"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "ppgnet learning point pair graph for line segment detection"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spacenet a remote sensing dataset and challenge series"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "roomnet end to end room layout estimation"

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)