human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "DAU-GAN: Unsupervised Object Transfiguration via Deep Attention Unit"):
  Abstract: Object transfiguration aims to translate objects in image from a kind to another, which is a subtask of image translation. Recently, researchers have proposed many effective approaches for object transfiguration. However, most of them ignore the difference between target objects and background, which would make background deformation, discolor and other problems. We propose a novel attention-based model for unsupervised object transfiguration called Deep Attention Units Generative Adversarial Network (DAU-GAN). We utilize spatial consistencies of objects and background to enable model to preserve background of image. Such an attention-based design enables DAU-GAN to enhance the expression of meaningful features and let the model able to distinguish specific objects and background in images. Experimental results demonstrate that our approach improves the performance of object transfiguration as well as effectively preserves background.

Includes members:
  Member "Deep Sparse Rectifier Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Maxout Networks")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science