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: "attend refine repeat active box proposal generation via in out localization"
  Abstract: The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work we propose a new approach to tackle this problem that is based on an active strategy for generating box proposals that starts from a set of seed boxes, which are uniformly distributed on the image, and then progressively moves its attention on the promising image areas where it is more likely to discover well localized bounding box proposals. We call our approach AttractioNet and a core component of it is a CNN-based category agnostic object location refinement module that is capable of yielding accurate and robust bounding box predictions regardless of the object category. #R##N#We extensively evaluate our AttractioNet approach on several image datasets (i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories. Furthermore, we evaluate our AttractioNet proposals in the context of the object detection task using a VGG16-Net based detector and the achieved detection performance on COCO manages to significantly surpass all other VGG16-Net based detectors while even being competitive with a heavily tuned ResNet-101 based detector. Code as well as box proposals computed for several datasets are available at:: this https URL

Connected through hyperedge HE-1 (induced by source paper "har net joint learning of hybrid attention for single stage object detection"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "feature selective anchor free module for single shot object detection"
    - "sharp attention network via adaptive sampling for person re identification"
    - "group normalization"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "pbrnet pyramidal bounding box refinement to improve object localization accuracy"):
  with members:
    - "double head rcnn rethinking classification and localization for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "side aware boundary localization for more precise object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "cascade r cnn high quality object detection and instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-4 (induced by source paper "hybrid task cascade for instance segmentation"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "cascade rpn delving into high quality region proposal network with adaptive convolution"):
  with members:
    - "region proposal by guided anchoring"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "region proposal by guided anchoring"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "metaanchor learning to detect objects with customized anchors"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "toward scale invariance and position sensitive region proposal networks"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-7 (induced by source paper "step spatio temporal progressive learning for video action detection"):
  with members:
    - "discovering spatio temporal action tubes"
    - "actor centric relation network"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "cascade r cnn delving into high quality object detection"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-8 (induced by source paper "rgb d image based object detection from traditional methods to deep learning techniques"):
  with members:
    - "improving image based localization with deep learning the impact of the loss function"
    - "deeply supervised salient object detection with short connections"
    - "geometric loss functions for camera pose regression with deep learning"
    - "rgbd salient object detection via deep fusion"
    - "numerical inversion of srnf maps for elastic shape analysis of genus zero surfaces"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"

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)