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 "indoor layout estimation by 2d lidar and camera fusion"):
  Abstract: This paper presents an algorithm for indoor layout estimation and reconstruction through the fusion of a sequence of captured images and LiDAR data sets. In the proposed system, a movable platform collects both intensity images and 2D LiDAR information. Pose estimation and semantic segmentation is computed jointly by aligning the LiDAR points to line segments from the images. For indoor scenes with walls orthogonal to floor, the alignment problem is decoupled into top-down view projection and a 2D similarity transformation estimation and solved by the recursive random sample consensus (R-RANSAC) algorithm. Hypotheses can be generated, evaluated and optimized by integrating new scans as the platform moves throughout the environment. The proposed method avoids the need of extensive prior training or a cuboid layout assumption, which is more effective and practical compared to most previous indoor layout estimation methods. Multi-sensor fusion allows the capability of providing accurate depth estimation and high resolution visual information.

Includes members:
  Member "edge semantic learning strategy for layout estimation in indoor environment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "deepperimeter indoor boundary estimation from posed monocular sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "floornet a unified framework for floorplan reconstruction from 3d scans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)