human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "semantic object accuracy for generative text to image synthesis"):
  Abstract: Generative adversarial networks conditioned on simple textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate images based on complex image captions from a heterogeneous domain. Furthermore, quantitatively evaluating these text-to-image synthesis models is still challenging, as most evaluation metrics only judge image quality but not the conformity between the image and its caption. To address the aforementioned challenges we introduce both a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption. Our model adds an object pathway to both the generator and the discriminator to explicitly learn features of individual objects. The SOA uses a pre-trained object detector to evaluate if a generated image contains objects that are specifically mentioned in the image caption, e.g. whether an image generated from "a car driving down the street" contains a car. Our evaluation shows that models which explicitly model individual objects outperform models which only model global image characteristics. However, the SOA also shows that despite this increased performance current models still struggle to generate images that contain realistic objects of multiple different domains.

Includes members:
  Member "generating multiple objects at spatially distinct locations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "interactive image generation using scene graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object centric image generation from layouts")
  Member "realistic image generation using region phrase attention":
    also connected through other hyperedges:
      (none)
  Member "object driven text to image synthesis via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "layoutgan generating graphic layouts with wireframe discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "auto completion of user interface layout design using transformer based tree decoders")
  Member "image synthesis from reconfigurable layout and style":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object centric image generation from layouts")
  Member "visual relation conscious image generation from structured text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "triplet aware scene graph embeddings")
  Member "classification accuracy score for conditional generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

Question: Please tell me which class the source paper 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)