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 "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping"):
  Abstract: Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird's-eye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford RobotCar Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.

Includes members:
  Member "deep object tracking on dynamic occupancy grid maps using rnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic environment prediction in urban scenes using recurrent representation learning")
  Member "learning to look around objects for top view representations of outdoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monolayout amodal scene layout from a single image")
  Member "compositional gan learning conditional image composition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive composition gan towards realistic image synthesis")
  Member "towards end to end lane detection an instance segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "cross view image synthesis using geometry guided conditional gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "compositional gan learning image conditional binary composition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive composition gan towards realistic image synthesis")
  Member "generative adversarial frontal view to bird view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross view semantic segmentation for sensing surroundings")
  Member "naturalistic driver intention and path prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")

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)