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: "ppgnet learning point pair graph for line segment detection"
  Abstract: In this paper, we present a novel framework to detect line segments in man-made environments. Specifically, we propose to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods. In order to extract a line segment graph from an image, we further introduce the PPGNet, a convolutional neural network that directly infers a graph from an image. We evaluate our method on published benchmarks including York Urban and Wireframe datasets. The results demonstrate that our method achieves satisfactory performance and generalizes well on all the benchmarks. The source code of our work is available at \url{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"
    - "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"
    - "learning attraction field representation for robust line segment detection"

Connected through hyperedge HE-2 (induced by source paper "deep hough transform for semantic line detection"):
  with members:
    - "learning spatial awareness to improve crowd counting"
    - "cornernet detecting objects as paired keypoints"
    - "context aware crowd counting"
    - "ccnet criss cross attention for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "non local neural networks"
    - "feature pyramid networks for object detection"
    - "pyramid scene parsing network"

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"
    - "deep high resolution representation learning for human pose estimation"
    - "end to end wireframe parsing"
    - "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 "sem lsd a learning based semantic line segment detector"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "end to end wireframe parsing"
    - "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-5 (induced by source paper "line as object datasets and framework for semantic line segment detection"):
  with members:
    - "end to end wireframe parsing"
    - "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-6 (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"
    - "end to end wireframe parsing"
    - "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-7 (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"
    - "end to end wireframe parsing"
    - "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)