human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "edge semantic learning strategy for layout estimation in indoor environment"
  Abstract: Visual cognition of the indoor environment can benefit from the spatial layout estimation, which is to represent an indoor scene with a 2D box on a monocular image. In this paper, we propose to fully exploit the edge and semantic information of a room image for layout estimation. More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image. We combine these two network predictions in a scoring function to evaluate the quality of the layouts, which are generated by ray sampling and from a predefined layout pool. Guided by the scoring function, we apply a novel refinement strategy to further optimize the layout hypotheses. Experimental results show that the proposed network can yield accurate estimates of edge maps and semantic labels. By fully utilizing the two different types of labels, the proposed method achieves state-of-the-art layout estimation performance on benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "flat2layout flat representation for estimating layout of general room types"
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "deep hough voting for 3d object detection in point clouds"
    - "deepperimeter indoor boundary estimation from posed monocular sequences"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (induced by source paper "indoor layout estimation by 2d lidar and camera fusion"):
  with members:
    - "deepperimeter indoor boundary estimation from posed monocular sequences"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"

Connected through hyperedge HE-3 (induced by source paper "semantic hierarchy emerges in the deep generative representations for scene synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "singan learning a generative model from a single natural image"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "semantic image synthesis with spatially adaptive normalization"
    - "interpreting the latent space of gans for semantic face editing"
    - "3d aware scene manipulation via inverse graphics"
    - "unified perceptual parsing for scene understanding"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "flat2layout flat representation for estimating layout of general room types"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "robot localization in floor plans using a room layout edge extraction network"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "panoroom from the sphere to the 3d layout"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-5 (induced by source paper "robot localization in floor plans using a room layout edge extraction network"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "floorplan priors for joint camera pose and room layout estimation"
    - "sedar semantic detection and ranging humans can localise without lidar can robots"
    - "identity mappings in deep residual networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Question: Please tell me which class the center node 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)