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: "text2scene generating compositional scenes from textual descriptions"
  Abstract: In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes (location, size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, including cartoon-like scenes, object layouts corresponding to real images, and synthetic images. Our method is not only competitive when compared with state-of-the-art GAN-based methods using automatic metrics and superior based on human judgments but also has the advantage of producing interpretable results.

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"

Connected through hyperedge HE-2 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only"):
  with members:
    - "multi marginal wasserstein gan"
    - "object driven text to image synthesis via adversarial training"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "semantics disentangling for text to image generation"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "to create what you tell generating videos from captions"
    - "image generation from scene graphs"

Connected through hyperedge HE-3 (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"
    - "image synthesis from reconfigurable layout and style"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "connecting vision and language with localized narratives"):
  with members:
    - "dense relational captioning triple stream networks for relationship based captioning"
    - "neural sequential phrase grounding seqground"
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale interactive object segmentation with human annotators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "context and attribute grounded dense captioning"

Connected through hyperedge HE-5 (induced by source paper "generating multiple objects at spatially distinct locations"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "deep structured generative models"
    - "yolov3 an incremental improvement"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "image generation from scene graphs"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "seq sg2sl inferring semantic layout from scene graph through sequence to sequence learning"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "centernet keypoint triplets for object detection"
    - "bridging the gap between training and inference for neural machine translation"
    - "mirrorgan learning text to image generation by redescription"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "image generation from scene graphs"
    - "image generation from layout"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "interactive image generation using scene graphs"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "an introduction to image synthesis with generative adversarial nets"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image generation from scene graphs"
    - "generating image sequence from description with lstm conditional gan"
    - "improved image captioning via policy gradient optimization of spider"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "image generation from layout"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "modular generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image generation from scene graphs"
    - "drit diverse image to image translation via disentangled representations"
    - "image to image translation for domain adaptation"

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)