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 "flat2layout flat representation for estimating layout of general room types"):
  Abstract: This paper proposes a new approach, Flat2Layout, for estimating general indoor room layout from a single-view RGB image whereas existing methods can only produce layout topologies captured from the box-shaped room. The proposed flat representation encodes the layout information into row vectors which are treated as the training target of the deep model. A dynamic programming based postprocessing is employed to decode the estimated flat output from the deep model into the final room layout. Flat2Layout achieves state-of-the-art performance on existing room layout benchmark. This paper also constructs a benchmark for validating the performance on general layout topologies, where Flat2Layout achieves good performance on general room types. Flat2Layout is applicable on more scenario for layout estimation and would have an impact on applications of Scene Modeling, Robotics, and Augmented Reality.

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 "horizonnet learning room layout with 1d representation and pano stretch data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the direction aware learnable additive kernels and the adversarial network for deep floor plan recognition")
  Member "robot localization in floor plans using a room layout edge extraction network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised model adaptation for multimodal semantic segmentation")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "dula net a dual projection network for estimating room layouts from a single rgb panorama":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the direction aware learnable additive kernels and the adversarial network for deep floor plan recognition")
  Member "panoroom from the sphere to the 3d layout":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "corners for layout end to end layout recovery from 360 images")
  Member "physics inspired optimization on semantic transfer features an alternative method for room layout estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ppgnet learning point pair graph for line segment detection")
  Member "joint layout estimation and global multi view registration for indoor reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "floorplan jigsaw jointly estimating scene layout and aligning partial scans")

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)