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: "visual relation conscious image generation from structured text"
  Abstract: We introduce visual-relation layout for image generation where relationships among entities from given structured-texts are explicitly employed for entities' localization. Our end-to-end network consists of the visual-relation layout module and the pyramid of GANs, namely stacking-GANs. The former firstly predicts a relation-unit for each relationship in a given text. It then unifies all the relation-units to produce the visual-relation layout, i.e., bounding-boxes for all the entities so that each of them uniquely corresponds to each entity while keeping its involved relationships. Accordingly, the visual-relation layout reflects the scene structure given in the input text. The latter is the stack of three GANs conditioned on the visual-relation layout and the output of previous GAN, consistently capturing the scene structure. Our network realistically renders entities' details in high resolution while keeping the scene structure. Experimental results on two public datasets show outperformances of our method against state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "classification accuracy score for conditional generative models"
    - "layoutvae stochastic scene layout generation from a label set"

Connected through hyperedge HE-2 (induced by source paper "triplet aware scene graph embeddings"):
  with members:
    - "interactive image generation using scene graphs"
    - "obj glove scene based contextual object embedding"
    - "layoutvae stochastic scene layout generation from a label set"
    - "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"

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)