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: "layoutvae stochastic scene layout generation from a label set"
  Abstract: Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignore plausible visual variations within the structure dictated by the text. We propose LayoutVAE, a variational autoencoder based framework for generating stochastic scene layouts. LayoutVAE is a versatile modeling framework that allows for generating full image layouts given a label set, or per label layouts for an existing image given a new label. In addition, it is also capable of detecting unusual layouts, potentially providing a way to evaluate layout generation problem. Extensive experiments on MNIST-Layouts and challenging COCO 2017 Panoptic dataset verifies the effectiveness of our proposed framework.

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"
    - "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"
    - "specifying object attributes and relations in interactive scene generation"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "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 "semantic object accuracy for generative text to image synthesis"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "interactive image generation using scene graphs"
    - "realistic image generation using region phrase attention"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "image synthesis from reconfigurable layout and style"
    - "visual relation conscious image generation from structured text"
    - "classification accuracy score for conditional generative models"

Connected through hyperedge HE-3 (induced by source paper "continuous graph flow for flexible density estimation"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"
    - "image generation from layout"

Connected through hyperedge HE-4 (induced by source paper "continuous graph flow"):
  with members:
    - "graph normalizing flows"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"
    - "image generation from layout"
    - "situation recognition with graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "triplet aware scene graph embeddings"):
  with members:
    - "interactive image generation using scene graphs"
    - "obj glove scene based contextual object embedding"
    - "visual relation conscious image generation from structured text"
    - "compact scene graphs for layout composition and patch retrieval"
    - "specifying object attributes and relations in interactive scene generation"
    - "linknet relational embedding for scene graph"
    - "graph r cnn for scene graph generation"
    - "learning to compose dynamic tree structures for visual contexts"

Connected through hyperedge HE-6 (induced by source paper "neural design network graphic layout generation with constraints"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "generating diverse high fidelity images with vq vae 2"
    - "dancing to music"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "graph r cnn for scene graph generation"
    - "a style based generator architecture for generative adversarial networks"
    - "image generation from scene graphs"

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)