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: "an analysis of pre training on object detection"
  Abstract: We provide a detailed analysis of convolutional neural networks which are pre-trained on the task of object detection. To this end, we train detectors on large datasets like OpenImagesV4, ImageNet Localization and COCO. We analyze how well their features generalize to tasks like image classification, semantic segmentation and object detection on small datasets like PASCAL-VOC, Caltech-256, SUN-397, Flowers-102 etc. Some important conclusions from our analysis are --- 1) Pre-training on large detection datasets is crucial for fine-tuning on small detection datasets, especially when precise localization is needed. For example, we obtain 81.1% mAP on the PASCAL-VOC dataset at 0.7 IoU after pre-training on OpenImagesV4, which is 7.6% better than the recently proposed DeformableConvNetsV2 which uses ImageNet pre-training. 2) Detection pre-training also benefits other localization tasks like semantic segmentation but adversely affects image classification. 3) Features for images (like avg. pooled Conv5) which are similar in the object detection feature space are likely to be similar in the image classification feature space but the converse is not true. 4) Visualization of features reveals that detection neurons have activations over an entire object, while activations for classification networks typically focus on parts. Therefore, detection networks are poor at classification when multiple instances are present in an image or when an instance only covers a small fraction of an image.

Connected through hyperedge HE-1 (induced by source paper "neural data server a large scale search engine for transfer learning data"):
  with members:
    - "active domain randomization"
    - "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"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-2 (induced by source paper "empirical upper bound in object detection and more"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "efficientdet scalable and efficient object detection"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "contextual relabelling of detected objects"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "object detection in 20 years a survey"

Connected through hyperedge HE-3 (induced by source paper "on the difficulty of warm starting neural network training"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "model based active exploration"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-4 (induced by source paper "pretraining boosts out of domain robustness for pose estimation"):
  with members:
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "simple baselines for human pose estimation and tracking"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-5 (induced by source paper "deep regionlets blended representation and deep learning for generic object detection"):
  with members:
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "cornernet detecting objects as paired keypoints"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "deep feature pyramid reconfiguration for object detection"
    - "multi scale location aware kernel representation for object detection"
    - "pseudo mask augmented object detection"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

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)