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: "unsupervised hard example mining from videos for improved object detection"
  Abstract: Important gains have recently been obtained in object detection by using training objectives that focus on {\em hard negative} examples, i.e., negative examples that are currently rated as positive or ambiguous by the detector. These examples can strongly influence parameters when the network is trained to correct them. Unfortunately, they are often sparse in the training data, and are expensive to obtain. In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences. In particular, detections that are {\em isolated in time}, i.e., that have no associated preceding or following detections, are likely to be hard negatives. We describe simple procedures for mining large numbers of such hard negatives (and also hard {\em positives}) from unlabeled video data. Our experiments show that retraining detectors on these automatically obtained examples often significantly improves performance. We present experiments on multiple architectures and multiple data sets, including face detection, pedestrian detection and other object categories.

Connected through hyperedge HE-1 (induced by source paper "model assertions for monitoring and improving ml model"):
  with members:
    - "better ai through logical scaffolding"
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "outside the box abstraction based monitoring of neural networks"
    - "scaling video analytics on constrained edge nodes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vstore a data store for analytics on large videos"
    - "selection via proxy efficient data selection for deep learning"
    - "formal security analysis of neural networks using symbolic intervals"

Connected through hyperedge HE-2 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-3 (induced by source paper "4d generic video object proposals"):
  with members:
    - "mots multi object tracking and segmentation"
    - "bayesian semantic instance segmentation in open set world"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combined image and world space tracking in traffic scenes"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "maskrnn instance level video object segmentation"
    - "pcn point completion network"

Connected through hyperedge HE-4 (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"
    - "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"
    - "data distillation towards omni supervised learning"

Connected through hyperedge HE-5 (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"
    - "active learning for deep 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-6 (induced by source paper "large scale object mining for object discovery from unlabeled video"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "in defense of the triplet loss for person re identification"
    - "object category learning and retrieval with weak supervision"
    - "track then decide category agnostic vision based multi object tracking"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "do image classifiers generalize across time"):
  with members:
    - "using videos to evaluate image model robustness"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "semantic adversarial examples"
    - "feature denoising for improving adversarial robustness"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generalisation in humans and deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "training object detectors with noisy data"):
  with members:
    - "distant vehicle detection using radar and vision"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to reweight examples for robust deep learning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "soft sampling for robust object detection"
    - "detecting traffic lights by single shot detection"
    - "focal loss for dense 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)