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: "training region based object detectors with online hard example mining"
  Abstract: The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always been – detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use. But more importantly, it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.

Connected through hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "character region awareness for text detection"
    - "deep visual template free form parsing"
    - "msr multi scale shape regression for scene text detection"
    - "rethinking table recognition using graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "gabor convolutional networks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"

Connected through hyperedge HE-3 (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-4 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "regularized evolution for image classifier architecture search"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-5 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "importance estimation for neural network pruning"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-6 (induced by source paper "semantic flow for fast and accurate scene parsing"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "customizable architecture search for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "panoptic feature pyramid networks"
    - "ocnet object context network for scene parsing"
    - "guided upsampling network for real time semantic segmentation"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "low latency video semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "puzzlenet scene text detection by segment context graph learning"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"

Connected through hyperedge HE-8 (induced by source paper "equalization loss for long tailed object recognition"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "decoupling representation and classifier for long tailed recognition"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "mask scoring r cnn"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "grid r cnn"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

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)