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: "grains generative recursive autoencoders for indoor scenes"
  Abstract: We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently. Our key observation is that indoor scene structures are inherently hierarchical. Hence, our network is not convolutional; it is a recursive neural network or RvNN. Using a dataset of annotated scene hierarchies, we train a variational recursive autoencoder, or RvNN-VAE, which performs scene object grouping during its encoding phase and scene generation during decoding. Specifically, a set of encoders are recursively applied to group 3D objects based on support, surround, and co-occurrence relations in a scene, encoding information about object spatial properties, semantics, and their relative positioning with respect to other objects in the hierarchy. By training a variational autoencoder (VAE), the resulting fixed-length codes roughly follow a Gaussian distribution. A novel 3D scene can be generated hierarchically by the decoder from a randomly sampled code from the learned distribution. We coin our method GRAINS, for Generative Recursive Autoencoders for INdoor Scenes. We demonstrate the capability of GRAINS to generate plausible and diverse 3D indoor scenes and compare with existing methods for 3D scene synthesis. We show applications of GRAINS including 3D scene modeling from 2D layouts, scene editing, and semantic scene segmentation via PointNet whose performance is boosted by the large quantity and variety of 3D scenes generated by our method.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "learning to infer and execute 3d shape programs"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-3 (induced by source paper "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"):
  with members:
    - "learning to group and label fine grained shape components"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "scores shape composition with recursive substructure priors"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-4 (induced by source paper "layoutvae stochastic scene layout generation from a label set"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "context aware synthesis and placement of object instances"

Connected through hyperedge HE-5 (induced by source paper "scenegraphnet neural message passing for 3d indoor scene augmentation"):
  with members:
    - "deep generative modeling for scene synthesis via hybrid representations"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "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-6 (induced by source paper "learning to generate new indoor scenes"):
  with members:
    - "scenegraphnet neural message passing for 3d indoor scene augmentation"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "read recursive autoencoders for document layout generation"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "scores shape composition with recursive substructure priors"
    - "a style based generator architecture for generative adversarial networks"
    - "mask r cnn"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "fast and flexible indoor scene synthesis via deep convolutional generative models"):
  with members:
    - "virtualhome simulating household activities via programs"
    - "deep generative modeling for scene synthesis via hybrid representations"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "chalet cornell house agent learning environment"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "3d prnn generating shape primitives with recurrent neural networks"

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.GR(Graphics)