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: "acquisition of localization confidence for accurate object detection"
  Abstract: Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS. In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective. Extensive experiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well as its compatibility with and adaptivity to several state-of-the-art object detectors.

Connected through hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation"):
  with members:
    - "a benchmark for anomaly segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "mask scoring r cnn"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "an alarm system for segmentation algorithm based on shape model"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"

Connected through hyperedge HE-3 (induced by source paper "cpm r cnn calibrating point guided misalignment in object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "grid r cnn plus faster and better"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

Connected through hyperedge HE-4 (induced by source paper "towards noise resistant object detection with noisy annotations"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "training object detectors with noisy data"
    - "unsupervised label noise modeling and loss correction"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"

Connected through hyperedge HE-5 (induced by source paper "1st place solutions for openimage2019 object detection and instance segmentation"):
  with members:
    - "revisiting the sibling head in object detector"
    - "detecting 11k classes large scale object detection without fine grained bounding boxes"
    - "double head rcnn rethinking classification and localization for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "cornernet detecting objects as paired keypoints"
    - "gradient harmonized single stage detector"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-6 (induced by source paper "inferring spatial uncertainty in object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "pixor real time 3d object detection from point clouds"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "iou loss for 2d 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "distance iou loss faster and better learning for bounding box regression"

Connected through hyperedge HE-7 (induced by source paper "differentiable fixed point iteration layer"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "differentiable convex optimization layers"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "optnet differentiable optimization as a layer in neural networks"
    - "input convex neural networks"
    - "structured prediction energy networks"
    - "flownet learning optical flow with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "cornernet lite efficient keypoint based object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "deformable convnets v2 more deformable better results"

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)