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 "semi local 3d lane detection and uncertainty estimation"):
  Abstract: We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation. Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments. It combines learning a parametric model for the segments along with a deep feature embedding that is then used to cluster segment together into full lanes. This combination allows our method to generalize to complex lane topologies, curvatures and surface geometries. Additionally, our method is the first to output a learning based uncertainty estimation for the lane detection task. The efficacy of our method is demonstrated in extensive experiments achieving state-of-the-art results for camera-based 3D lane detection, while also showing our ability to generalize to complex topologies, curvatures and road geometries as well as to different cameras. We also demonstrate how our uncertainty estimation aligns with the empirical error statistics indicating that it is well calibrated and truly reflects the detection noise.

Includes members:
  Member "end to end lane detection through differentiable least squares fitting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lane detection in low light conditions using an efficient data enhancement light conditions style transfer")
  Member "deep multi sensor lane detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust multi modality multi object tracking")
  Member "learning lightweight lane detection cnns by self attention distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lane detection in low light conditions using an efficient data enhancement light conditions style transfer")
  Member "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "evaluating and calibrating uncertainty prediction in regression tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluating scalable uncertainty estimation methods for dnn based molecular property prediction")
  Member "monoloco monocular 3d pedestrian localization and uncertainty estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "a mixed classification regression framework for 3d pose estimation from 2d images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "towards end to end lane detection an instance segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "detecting lane and road markings at a distance with perspective transformer layers")

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)