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: "fast and flexible indoor scene synthesis via deep convolutional generative models"
  Abstract: We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predicting their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes. Our method is significantly faster than the previous image-based method and generates result that outperforms it and other state-of-the-art deep generative scene models in terms of faithfulness to training data and perceived visual quality.

Connected through hyperedge HE-1 (induced by source paper "house gan relational generative adversarial networks for graph constrained house layout generation"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "layoutvae stochastic scene layout generation from a label set"
    - "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"
    - "specifying object attributes and relations in interactive scene generation"
    - "image generation from scene graphs"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "fast 3d indoor scene synthesis with discrete and exact layout pattern extraction"):
  with members:
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "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"
    - "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 "extreme relative pose network under hybrid representations"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "extreme relative pose estimation for rgb d scans via scene completion"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-4 (induced by source paper "scenegraphnet neural message passing for 3d indoor scene augmentation"):
  with members:
    - "deep generative modeling for scene synthesis via hybrid representations"
    - "grains generative recursive autoencoders for indoor scenes"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semantic scene completion from a single depth image"
    - "gated graph sequence neural networks"
    - "interaction networks for learning about objects relations and physics"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-5 (induced by source paper "learning to generate new indoor scenes"):
  with members:
    - "scenegraphnet neural message passing for 3d indoor scene augmentation"
    - "grains generative recursive autoencoders for indoor scenes"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "semantic scene completion from a single depth image"
    - "hierarchical question image co attention for visual question answering"

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)