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: "fast interactive object annotation with curve gcn"
  Abstract: Manually labeling objects by tracing their boundaries is a laborious process. In Polygon-RNN++ the authors proposed Polygon-RNN that produces polygonal annotations in a recurrent manner using a CNN-RNN architecture, allowing interactive correction via humans-in-the-loop. We propose a new framework that alleviates the sequential nature of Polygon-RNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN). Our model is trained end-to-end. It supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved objects. We show that Curve-GCN outperforms all existing approaches in automatic mode, including the powerful PSP-DeepLab and is significantly more efficient in interactive mode than Polygon-RNN++. Our model runs at 29.3ms in automatic, and 2.6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.

Connected through hyperedge HE-1 (induced by source paper "deep snake for real time instance segmentation"):
  with members:
    - "dense reppoints representing visual objects with dense point sets"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "objects as points"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "explicit shape encoding for real time instance segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"

Connected through hyperedge HE-2 (induced by source paper "icap interative image captioning with predictive text"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "annotating object instances with a polygon rnn"
    - "twin networks matching the future for sequence generation"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "self critical sequence training for image captioning"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (induced by source paper "gated scnn gated shape cnns for semantic segmentation"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "adaptive affinity fields for semantic segmentation"
    - "free form image inpainting with gated convolution"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tree structured kronecker convolutional network for semantic segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-4 (induced by source paper "polytransform deep polygon transformer for instance segmentation"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "hierarchical attention networks for medical image segmentation"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "patch based output space adversarial learning for joint optic disc and cup segmentation"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "a simple pooling based design for real time salient object detection"
    - "ce net context encoder network for 2d medical image segmentation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "autolabeling 3d objects with differentiable rendering of sdf shape priors"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "disentangling monocular 3d object detection"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "predicting 3d human dynamics from video"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "neural turtle graphics for modeling city road layouts"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "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-8 (induced by source paper "end to end trainable active contours via differentiable rendering"):
  with members:
    - "cnn based semantic segmentation using level set loss"
    - "darnet deep active ray network for building segmentation"
    - "mesh r cnn"
    - "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"

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)