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 "groundnet segmentation aware monocular ground plane estimation with geometric consistency"):
  Abstract: We focus on the problem of estimating the orientation of the ground plane with respect to a mobile monocular camera platform (e.g., ground robot, wearable camera, assistive robotic platform). To address this problem, we formulate the ground plane estimation problem as an inter-mingled multi-task prediction problem by jointly optimizing for point-wise surface normal direction, 2D ground segmentation, and depth estimates. Our proposed model -- GroundNet -- estimates the ground normal in two streams separately and then a consistency loss is applied on top of the two streams to enforce geometric consistency. A semantic segmentation stream is used to isolate the ground regions and are used to selectively back-propagate parameter updates only through the ground regions in the image. Our experiments on KITTI and ApolloScape datasets verify that the GroundNet is able to predict consistent depth and normal within the ground region. It also achieves top performance on ground plane normal estimation and horizon line detection.

Includes members:
  Member "a baseline for 3d multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points")
  Member "monocular 3d object detection with pseudo lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "learning correspondence from the cycle consistency of time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "unsupervised deep tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "future near collision prediction from monocular video feasibility dataset and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequential forecasting of 100 000 points")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "deep reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)