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: "layouts from panoramic images with geometry and deep learning"
  Abstract: In this paper, we propose a novel procedure for 3D layout recovery of indoor scenes from single 360 degrees panoramic images. With such images, all scene is seen at once, allowing to recover closed geometries. Our method combines strategically the accuracy provided by geometric reasoning (lines and vanishing points) with the higher level of data abstraction and pattern recognition achieved by deep learning techniques (edge and normal maps). Thus, we extract structural corners from which we generate layout hypotheses of the room assuming Manhattan world. The best layout model is selected, achieving good performance on both simple rooms (box-type) and complex shaped rooms (with more than four walls). Experiments of the proposed approach are conducted within two public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of estimating layouts by combining geometry and deep learning and the effectiveness of our proposal with respect to the state of the art.

Connected through hyperedge HE-1 (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"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "deformable convolutional networks"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-2 (induced by source paper "what s in my room object recognition on indoor panoramic images"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "blitznet a real time deep network for scene understanding"
    - "mask r cnn"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (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"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "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-4 (induced by source paper "panoroom from the sphere to the 3d layout"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "predictive and semantic layout estimation for robotic applications in manhattan worlds"):
  with members:
    - "simultaneous localization and layout model selection in manhattan worlds"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "roomnet end to end room layout estimation"
    - "factoring shape pose and layout from the 2d image of a 3d scene"
    - "semantic scene completion from a single depth image"

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)