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: "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
  Abstract: In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural networks; (2) an architecture that can be end-to-end trained; (3) a practical strategy to initialize weights for very deep networks under unbalanced training data distribution. ST allows us to extract highly robust features under various circumstances, and in order to address the computation redundance hidden in these features we develop a principled and efficient inference scheme named physics inspired optimization (PIO). PIO's basic idea is to formulate some phenomena observed in ST features into mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the proposed method is more accurate than state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "ppgnet learning point pair graph for line segment detection"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "unified perceptual parsing for scene understanding"
    - "scene graph generation by iterative message passing"
    - "roomnet end to end room layout estimation"
    - "pixels to graphs by associative embedding"
    - "mask r cnn"
    - "semantic understanding of scenes through the ade20k dataset"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "corners for layout end to end layout recovery from 360 images"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "omnidrl robust pedestrian detection using deep reinforcement learning on omnidirectional cameras"
    - "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"
    - "layouts from panoramic images with geometry and deep learning"
    - "deformable convolutional networks"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-3 (induced by source paper "flat2layout flat representation for estimating layout of general room types"):
  with members:
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "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"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-4 (induced by source paper "3d manhattan room layout reconstruction from a single 360 image"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "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"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "complete 3d scene parsing from an rgbd image"
    - "roomnet end to end room layout estimation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-5 (induced by source paper "horizonnet learning room layout with 1d representation and pano stretch data augmentation"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "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"
    - "layouts from panoramic images with geometry and deep learning"
    - "roomnet end to end room layout estimation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "pano2cad room layout from a single panorama image"

Connected through hyperedge HE-6 (induced by source paper "smart hypothesis generation for efficient and robust room layout estimation"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "roomnet end to end room layout estimation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "edge semantic learning strategy for layout estimation in indoor environment"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "roomnet end to end room layout estimation"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "holistic 3d scene parsing and reconstruction from a single rgb image"):
  with members:
    - "human centric indoor scene synthesis using stochastic grammar"
    - "soft nms improving object detection with one line of code"
    - "deformable convolutional networks"
    - "complete 3d scene parsing from an rgbd image"
    - "roomnet end to end room layout estimation"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"

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)