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: "large scale visual active learning with deep probabilistic ensembles"
  Abstract: Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provide an effective solution to this. Despite being theoretically principled, BNNs require approximations to be applied to large-scale problems, where both performance and uncertainty estimation are crucial. In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep BNN. We conduct a series of large-scale visual active learning experiments to evaluate DPEs on classification with the CIFAR-10, CIFAR-100 and ImageNet datasets, and semantic segmentation with the BDD100k dataset. Our models require significantly less training data to achieve competitive performances, and steadily improve upon strong active learning baselines as the annotation budget is increased.

Connected through hyperedge HE-1 (induced by source paper "viewal active learning with viewpoint entropy for semantic segmentation"):
  with members:
    - "learning loss for active learning"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cost effective active learning for melanoma segmentation"
    - "cost effective active learning for deep image classification"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (induced by source paper "deep active learning for efficient training of a lidar 3d object detector"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "localization aware active learning for object detection"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (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"
    - "active learning for deep object detection"
    - "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-4 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "automl a survey of the state of the art"
    - "are all training examples created equal an empirical study"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"

Connected through hyperedge HE-5 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "deep clustering for unsupervised learning of visual features"
    - "revisiting pre training an efficient training method for image classification"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-6 (induced by source paper "less is more an exploration of data redundancy with active dataset subsampling"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "are all training examples created equal an empirical study"
    - "practical obstacles to deploying active learning"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"
    - "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"
    - "revisiting unreasonable effectiveness of data in deep learning era"

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)