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 "predictive and semantic layout estimation for robotic applications in manhattan worlds"):
  Abstract: This paper describes an approach to automatically extracting floor plans from the kinds of incomplete measurements that could be acquired by an autonomous mobile robot. The approach proceeds by reasoning about extended structural layout surfaces which are automatically extracted from the available data. The scheme can be run in an online manner to build water tight representations of the environment. The system effectively speculates about room boundaries and free space regions which provides useful guidance to subsequent motion planning systems. Experimental results are presented on multiple data sets.

Includes members:
  Member "simultaneous localization and layout model selection in manhattan worlds":
    also connected through other hyperedges:
      (none)
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "layouts from panoramic images with geometry and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "corners for layout end to end layout recovery from 360 images")
  Member "roomnet end to end room layout estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "shallow2deep indoor scene modeling by single image understanding")
  Member "factoring shape pose and layout from the 2d image of a 3d scene":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "synsin end to end view synthesis from a single image")
  Member "semantic scene completion from a single depth image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

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)