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 "elegant exchanging latent encodings with gan for transferring multiple face attributes"):
  Abstract: Recent studies on face attribute transfer have achieved great success, especially after the emergence of generative adversarial networks (GANs). A lot of image-to-image translation models are able to transfer face attributes given an input image. However, they suffer from three limitations: (1) failing to make image generation by exemplars; (2) unable to deal with multiple face attributes simultaneously; (3) low-quality generated images, such as low resolution and sensible artifacts. To address these limitations, we propose a novel model that receives two images of different attributes as inputs. Our model can transfer the exactly same type of attributes from one image to another by exchanging certain part of their encodings. All the attributes are encoded in the latent space in a disentangled manner, which enables us to manipulate several attributes simultaneously. Besides, it can learn the residual images so as to facilitate training on higher resolution images. With the help of multi-scale discriminators for adversarial training, it can even generate high-quality images with finer details and less artifacts. We demonstrate the effectiveness of our model in overcoming the above three limitations by comparing with other methods on the CelebA face database.

Includes members:
  Member "modular generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards unsupervised learning of generative models for 3d controllable image synthesis")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "dna gan learning disentangled representations from multi attribute images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised stylegan for disentanglement learning")
  Member "genegan learning object transfiguration and attribute subspace from unpaired data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation")
  Member "learning to discover cross domain relations with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "unsupervised image to image translation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "toward multimodal image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)