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: "dropout sampling for robust object detection in open set conditions"
  Abstract: Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling. We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision. We evaluate this approach on a large synthetic dataset with 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment.

Connected through hyperedge HE-1 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving"):
  with members:
    - "deep evidential regression"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "learning multimodal fixed point weights using gradient descent"
    - "pixor real time 3d object detection from point clouds"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction"):
  with members:
    - "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "learning single camera depth estimation using dual pixels"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "uncertainty estimation in one stage object detection"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "gated2depth real time dense lidar from gated images"

Connected through hyperedge HE-3 (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"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "bayesod a bayesian approach for uncertainty estimation in deep object detectors"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "uncertainty estimation in one stage object detection"
    - "evaluating merging strategies for sampling based uncertainty techniques in object detection"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "the limits and potentials of deep learning for robotics"
    - "bounding box regression with uncertainty for accurate object detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"

Connected through hyperedge HE-5 (induced by source paper "can we trust you on calibration of a probabilistic object detector for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "learning multimodal fixed point weights using gradient descent"
    - "pixor real time 3d object detection from point clouds"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"

Connected through hyperedge HE-6 (induced by source paper "uncertainty estimation in one stage object detection"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "yolov3 an incremental improvement"
    - "calibrating uncertainties in object localization task"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "the probabilistic object detection challenge"):
  with members:
    - "probabilistic object detection definition and evaluation"
    - "vqa visual question answering"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "probabilistic object detection definition and evaluation"):
  with members:
    - "evaluating merging strategies for sampling based uncertainty techniques in object detection"
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "localization recall precision lrp a new performance metric for object detection"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "on calibration of modern neural networks"

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)