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: "image synthesis from reconfigurable layout and style"
  Abstract: Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i.e., bounding boxes + class labels in an image lattice) and style (i.e., structural and appearance variations encoded by latent vectors), especially at high resolution. By reconfigurable, it means that a model can preserve the intrinsic one-to-many mapping from a given layout to multiple plausible images with different styles, and is adaptive with respect to perturbations of a layout and style latent code. In this paper, we present a layout- and style-based architecture for generative adversarial networks (termed LostGANs) that can be trained end-to-end to generate images from reconfigurable layout and style. Inspired by the vanilla StyleGAN, the proposed LostGAN consists of two new components: (i) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images, and (ii) learning object instance-specific layout-aware feature normalization (ISLA-Norm) in the generator to realize multi-object style generation. In experiments, the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. The code and pretrained models are available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "specifying object attributes and relations in interactive scene generation"

Connected through hyperedge HE-2 (induced by source paper "learning canonical representations for scene graph to image generation"):
  with members:
    - "spatio temporal action graph networks"
    - "interactive image generation using scene graphs"
    - "on the universality of invariant networks"
    - "mirrorgan learning text to image generation by redescription"
    - "text guided person image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "specifying object attributes and relations in interactive scene generation"

Connected through hyperedge HE-3 (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"
    - "visual relation conscious image generation from structured text"
    - "classification accuracy score for conditional generative models"
    - "layoutvae stochastic scene layout generation from a label set"

Connected through hyperedge HE-4 (induced by source paper "attentive normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "instance level meta normalization"
    - "searching for mobilenetv3"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fixup initialization residual learning without normalization"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "bag of tricks for image classification with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "visual relation conscious image generation from structured text"):
  with members:
    - "pastegan a semi parametric method to generate image from scene graph"
    - "using scene graph context to improve image generation"
    - "specifying object attributes and relations in interactive scene generation"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "the contextual loss for image transformation with non aligned data"
    - "image generation from scene graphs"
    - "perceptual adversarial networks for image to image transformation"
    - "image generation from layout"

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)