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: "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
  Abstract: The status quo approach to training object detectors requires expensive bounding box annotations. Our framework takes a markedly different direction: we transfer tracked object boxes from weakly-labeled videos to weakly-labeled images to automatically generate pseudo ground-truth boxes, which replace manually annotated bounding boxes. We first mine discriminative regions in the weakly-labeled image collection that frequently/rarely appear in the positive/negative images. We then match those regions to videos and retrieve the corresponding tracked object boxes. Finally, we design a hough transform algorithm to vote for the best box to serve as the pseudo GT for each image, and use them to train an object detector. Together, these lead to state-of-the-art weakly-supervised detection results on the PASCAL 2007 and 2010 datasets.

Connected through hyperedge HE-1 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-2 (induced by source paper "automatic adaptation of object detectors to new domains using self training"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "unsupervised hard example mining from videos for improved object detection"
    - "theory of curriculum learning with convex loss functions"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "domain adaptation for visual applications a comprehensive survey"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"):
  with members:
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "exploring the limits of weakly supervised pretraining"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "learning explanatory rules from noisy data"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "from lifestyle vlogs to everyday interactions"
    - "end to end differentiable proving"

Connected through hyperedge HE-4 (induced by source paper "context aware emotion recognition networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "cbam convolutional block attention module"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "facial expression recognition from world wild web"
    - "deep residual learning for image recognition"
    - "action recognition using visual attention"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "unsupervised hard example mining from videos for improved object detection"):
  with members:
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "s 3 fd single shot scale invariant face detector"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "detecting faces using region based fully convolutional networks"
    - "fine tuning deep cnn models on specific ms coco categories"
    - "face detection using deep learning an improved faster rcnn approach"

Connected through hyperedge HE-6 (induced by source paper "visual and semantic knowledge transfer for large scale semi supervised object detection"):
  with members:
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "hide and seek a data augmentation technique for weakly supervised localization and beyond"):
  with members:
    - "random erasing data augmentation"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "wide residual networks"
    - "weakly supervised deep detection networks"
    - "do deep neural networks learn facial action units when doing expression recognition"

Connected through hyperedge HE-8 (induced by source paper "lstd a low shot transfer detector for object detection"):
  with members:
    - "few example object detection with model communication"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional networks"
    - "matching networks for one shot learning"
    - "few shot object recognition from machine labeled web images"
    - "contextlocnet context aware deep network models for weakly supervised localization"

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)