human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bayesod a bayesian approach for uncertainty estimation in deep object detectors"):
  Abstract: When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs) have been proposed in recent works, but have had limited success due to 1) information loss at the detectors non-maximum suppression (NMS) stage, and 2) failure to take into account the multitask, many-to-one nature of anchor-based object detection. To that end, we introduce BayesOD, an uncertainty estimation approach that reformulates the standard object detector inference and Non-Maximum suppression components from a Bayesian perspective. Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77\%-13.13\% on the minimum Gaussian uncertainty error metric and a reduction of 1.63\%-5.23\% on the minimum Categorical uncertainty error metric. Code will be released at {\url{this https URL}}.

Includes members:
  Member "lasernet an efficient probabilistic 3d object detector for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "uncertainty estimation in one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")
  Member "evaluating merging strategies for sampling based uncertainty techniques in object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "the limits and potentials of deep learning for robotics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "bounding box regression with uncertainty for accurate object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "bdd100k a diverse driving video database with scalable annotation tooling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")

Question: Please tell me which class the source paper 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)