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: "bag of freebies for training object detection neural networks"
  Abstract: Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}. Object detection models, however, have more complex neural network structures and optimization targets. The training strategies and pipelines dramatically vary among different models. In this works, we explore training tweaks that apply to various models including Faster R-CNN and YOLOv3. These tweaks do not change the model architectures, therefore, the inference costs remain the same. Our empirical results demonstrate that, however, these freebies can improve up to 5% absolute precision compared to state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "rethinking image mixture for unsupervised visual representation learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "improved baselines with momentum contrastive learning"
    - "contrastive multiview coding"
    - "when does label smoothing help"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "mixup regularization for region proposal based object detectors"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "towards adversarially robust object detection"
    - "learning to generate synthetic data via compositing"
    - "on physical adversarial patches for object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "regularization for deep learning a taxonomy"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "learning spatial fusion for single shot object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "adaptively connected neural networks"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "objects as points"

Connected through hyperedge HE-4 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources"):
  with members:
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "an empirical model of large batch training"
    - "low latency video semantic segmentation"
    - "three factors influencing minima in sgd"
    - "slowfast networks for video recognition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "tvqa localized compositional video question answering"

Connected through hyperedge HE-5 (induced by source paper "adaptis adaptive instance selection network"):
  with members:
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "a style based generator architecture for generative adversarial networks"
    - "panoptic segmentation with a joint semantic and instance segmentation network"

Connected through hyperedge HE-6 (induced by source paper "testing deep learning models for image analysis using object relevant metamorphic relations"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "cornernet lite efficient keypoint based object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "yolov3 an incremental improvement"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "simple baselines for human pose estimation and tracking"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-7 (induced by source paper "dubox no prior box objection detection via residual dual scale detectors"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "sface an efficient network for face detection in large scale variations"
    - "derpn taking a further step toward more general object detection"
    - "cbam convolutional block attention module"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "a unified neural network for object detection multiple object tracking and vehicle re identification"):
  with members:
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "fcos fully convolutional one stage object detection"
    - "focal loss for dense object detection"
    - "margin sample mining loss a deep learning based method for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "simple online and realtime tracking with a deep association metric"

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)