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: "learning to reconstruct 3d manhattan wireframes from a single image"
  Abstract: In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities. Our method trains a convolutional neural network to simultaneously detect salient junctions and straight lines, as well as predict their 3D depth and vanishing points. Compared with the state-of-the-art learning-based wireframe detection methods, our network is much simpler and more unified, leading to better 2D wireframe detection. With global structural priors such as Manhattan assumption, our method further reconstructs a full 3D wireframe model, a compact vector representation suitable for a variety of high-level vision tasks such as AR and CAD. We conduct extensive evaluations on a large synthetic dataset of urban scenes as well as real images. Our code and datasets will be released.

Connected through hyperedge HE-1 (induced by source paper "learning structure appearance joint embedding for indoor scene image synthesis"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "end to end wireframe parsing"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "learning attraction field representation for robust line segment detection"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semi parametric image synthesis"

Connected through hyperedge HE-2 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "3d manhattan room layout reconstruction from a single 360 image"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "planenet piece wise planar reconstruction from a single rgb image"

Connected through hyperedge HE-3 (induced by source paper "neurvps neural vanishing point scanning via conic convolution"):
  with members:
    - "spherical cnns on unstructured grids"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "end to end wireframe parsing"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "active convolution learning the shape of convolution for image classification"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "deformable convolutional networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"

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)