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 "learning structure appearance joint embedding for indoor scene image synthesis"):
  Abstract: Advanced image synthesis methods can generate photo-realistic images for faces, birds, bedrooms, and more. However, these methods do not explicitly model and preserve essential structural constraints such as junctions, parallel lines, and planar surfaces. In this paper, we study the problem of structured indoor image generation for design applications. We utilize a small-scale dataset that contains both images of various indoor scenes and their corresponding ground-truth wireframe annotations. While existing image synthesis models trained on the dataset are insufficient in preserving structural integrity, we propose a novel model based on a structure-appearance joint embedding learned from both images and wireframes. In our model, structural constraints are explicitly enforced by learning a joint embedding in a shared encoder network that must support the generation of both images and wireframes. We demonstrate the effectiveness of the joint embedding learning scheme on the indoor scene wireframe to image translation task. While wireframes as input contain less semantic information than inputs of other traditional image translation tasks, our model can generate high fidelity indoor scene renderings that match well with input wireframes. Experiments on a wireframe-scene dataset show that our proposed translation model significantly outperforms existing state-of-the-art methods in both visual quality and structural integrity of generated images.

Includes members:
  Member "learning to reconstruct 3d manhattan wireframes from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "end to end wireframe parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "holistically attracted wireframe parsing")
  Member "learning to predict layout to image conditional convolutions for semantic image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "learning attraction field representation for robust line segment detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "sketchygan towards diverse and realistic sketch to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)