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: "what makes for effective detection proposals"
  Abstract: Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detection performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.

Connected through hyperedge HE-1 (induced by source paper "deeply activated salient region for instance search"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-2 (induced by source paper "learning to group a bottom up framework for 3d part discovery in unseen categories"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deep part induction from articulated object pairs"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "3d shape segmentation via shape fully convolutional networks"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-3 (induced by source paper "bihl a fast and high performance object proposals based on binarized hl frequency"):
  with members:
    - "cascade r cnn delving into high quality object detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "mask r cnn"
    - "learning to segment object candidates"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "firearm detection and segmentation using an ensemble of semantic neural networks"):
  with members:
    - "automatic handgun detection alarm in videos using deep learning"
    - "agent agnostic human in the loop reinforcement learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep neural networks for marine debris detection in sonar images"):
  with members:
    - "on the convergence of adam and beyond"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "learning transferable architectures for scalable image recognition"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-6 (induced by source paper "eurocity persons a novel benchmark for person detection in traffic scenes"):
  with members:
    - "yolov3 an incremental improvement"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "soft nms improving object detection with one line of code"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"
    - "learning non maximum suppression"
    - "perceptual generative adversarial networks for small object detection"

Connected through hyperedge HE-7 (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-8 (induced by source paper "where are the masks instance segmentation with image level supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "instance segmentation with point supervision"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "yolact real time instance segmentation"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "where are the blobs counting by localization with point supervision"

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)