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: "active learning for deep object detection"
  Abstract: The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside from existing benchmarks is both challenging and costly. Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable continuous exploration of new unlabeled datasets. We propose a set of uncertainty-based active learning metrics suitable for most object detectors. Furthermore, we present an approach to leverage class imbalances during sample selection. All methods are evaluated systematically in a continuous exploration context on the PASCAL VOC 2012 dataset.

Connected through hyperedge HE-1 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-2 (induced by source paper "temporal coherence for active learning in videos"):
  with members:
    - "learning loss for active learning"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "unsupervised hard example mining from videos for improved object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "the elephant in the room"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-3 (induced by source paper "an adaptive supervision framework for active learning in object detection"):
  with members:
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "deep bayesian active learning with image data"
    - "mask r cnn"
    - "training object class detectors with click supervision"
    - "soft proposal networks for weakly supervised object localization"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "we don t need no bounding boxes training object class detectors using only human verification"

Connected through hyperedge HE-4 (induced by source paper "organization of ml based product development as per iso 26262"):
  with members:
    - "self driving cars a survey"
    - "a review of deep learning with special emphasis on architectures applications and recent trends"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "runtime monitoring neuron activation patterns"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "lanenet real time lane detection networks for autonomous driving"
    - "a survey of safety and trustworthiness of deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-5 (induced by source paper "lost a flexible framework for semi automatic image annotation"):
  with members:
    - "freelabel a publicly available annotation tool based on freehand traces"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "mask editor an image annotation tool for image segmentation tasks"
    - "focal loss for dense object detection"
    - "extreme clicking for efficient object annotation"
    - "training object class detectors with click supervision"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "active learning with weak supervision for cost effective panicle detection in cereal crops"):
  with members:
    - "deep learning in agriculture a survey"
    - "cost effective active learning for deep image classification"
    - "deep bayesian active learning with image data"
    - "real time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns"
    - "mask r cnn"
    - "training object class detectors with click supervision"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "active learning for deep detection neural networks"):
  with members:
    - "deep visual domain adaptation a survey"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "rethinking atrous convolution for semantic image segmentation"
    - "feature pyramid networks for object detection"

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)