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: "efficient interactive annotation of segmentation datasets with polygon rnn"
  Abstract: Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we design a new CNN encoder architecture, 2) show how to effectively train the model with Reinforcement Learning, and 3) significantly increase the output resolution using a Graph Neural Network, allowing the model to accurately annotate high-resolution objects in images. Extensive evaluation on the Cityscapes dataset shows that our model, which we refer to as Polygon-RNN++, significantly outperforms the original model in both automatic (10% absolute and 16% relative improvement in mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We further analyze the cross-domain scenario in which our model is trained on one dataset, and used out of the box on datasets from varying domains. The results show that Polygon-RNN++ exhibits powerful generalization capabilities, achieving significant improvements over existing pixel-wise methods. Using simple online fine-tuning we further achieve a high reduction in annotation time for new datasets, moving a step closer towards an interactive annotation tool to be used in practice.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"
    - "collaborative annotation of semantic objects in images with multi granularity supervisions"

Connected through hyperedge HE-2 (induced by source paper "neural data server a large scale search engine for transfer learning data"):
  with members:
    - "active domain randomization"
    - "an analysis of pre training on object detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "meta sim learning to generate synthetic datasets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "learning to generate synthetic data via compositing"
    - "modanet a large scale street fashion dataset with polygon annotations"

Connected through hyperedge HE-3 (induced by source paper "sdod real time segmenting and detecting 3d objects by depth"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "yolact better real time instance segmentation"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "solo segmenting objects by locations"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (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"
    - "fast interactive object annotation with curve gcn"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "icap interative image captioning with predictive text"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "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-6 (induced by source paper "segmentation of objects by hashing"):
  with members:
    - "searching for efficient multi scale architectures for dense image prediction"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "practical and optimal lsh for angular distance"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "plant stem segmentation using fast ground truth generation"):
  with members:
    - "joint stem detection and crop weed classification for plant specific treatment in precision farming"
    - "mask r cnn"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (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"
    - "fast interactive object annotation with curve gcn"
    - "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"

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)