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: "darnet deep active ray network for building segmentation"
  Abstract: In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (CNN) as the backbone to predict energy maps, which are further utilized to construct an energy function. A polygon-based contour is then evolved via minimizing the energy function, of which the minimum defines the final segmentation. Instead of parameterizing the contour using Euclidean coordinates, we adopt polar coordinates, i.e., rays, which not only prevents self-intersection but also simplifies the design of the energy function. Moreover, we propose a loss function that directly encourages the contours to match building boundaries. Our DARNet is trained end-to-end by back-propagating through the energy minimization and the backbone CNN, which makes the CNN adapt to the dynamics of the contour evolution. Experiments on three building instance segmentation datasets demonstrate our DARNet achieves either state-of-the-art or comparable performances to other competitors.

Connected through hyperedge HE-1 (induced by source paper "polytransform deep polygon transformer for instance segmentation"):
  with members:
    - "panoptic segmentation"
    - "video instance segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "hybrid task cascade for instance segmentation"
    - "deep rigid instance scene flow"

Connected through hyperedge HE-2 (induced by source paper "dense reppoints representing visual objects with dense point sets"):
  with members:
    - "reppoints point set representation for object detection"
    - "pointrend image segmentation as rendering"
    - "centernet keypoint triplets for object detection"
    - "polarmask single shot instance segmentation with polar representation"
    - "tensormask a foundation for dense object segmentation"
    - "yolact real time instance segmentation"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-3 (induced by source paper "neural turtle graphics for modeling city road layouts"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "topological map extraction from overhead images"

Connected through hyperedge HE-4 (induced by source paper "end to end trainable active contours via differentiable rendering"):
  with members:
    - "cnn based semantic segmentation using level set loss"
    - "mesh r cnn"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "improved breast mass segmentation in mammograms with conditional residual u net"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "adversarial deep structured nets for mass segmentation from mammograms"

Connected through hyperedge HE-5 (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"
    - "ppgnet learning point pair graph for line segment detection"
    - "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-6 (induced by source paper "vectorizing world buildings planar graph reconstruction by primitive detection and relationship classification"):
  with members:
    - "ppgnet learning point pair graph for line segment detection"
    - "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)