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: "structured3d a large photo realistic dataset for structured 3d modeling"
  Abstract: Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often obtained via human labor, which is particularly challenging and inefficient for such tasks due to the large number of 3D structure instances (e.g., line segments) and other factors such as viewpoints and occlusions. In this paper, we present a new synthetic dataset, Structured3D, with the aim to providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks. We take advantage of the availability of millions of professional interior designs and automatically extract 3D structures from them. We generate high-quality images with an industry-leading rendering engine. We use our synthetic dataset in combination with real images to train deep neural networks for room layout estimation and demonstrate improved 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"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "flat2layout flat representation for estimating layout of general room types"
    - "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 "fast 3d indoor scene synthesis with discrete and exact layout pattern extraction"):
  with members:
    - "fast and scalable position based layout synthesis"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "grains generative recursive autoencoders for indoor scenes"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "automatic generation of constrained furniture layouts"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "style compatible object recommendation for multi room indoor scene synthesis"):
  with members:
    - "fast and scalable position based layout synthesis"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "semantic scene completion from a single depth image"
    - "neural autoregressive distribution estimation"

Connected through hyperedge HE-4 (induced by source paper "towards unsupervised learning of generative models for 3d controllable image synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "a layer based sequential framework for scene generation with gans"
    - "monet unsupervised scene decomposition and representation"
    - "singan learning a generative model from a single natural image"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "multi object representation learning with iterative variational inference"

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)