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 "face video generation from a single image and landmarks"):
  Abstract: In this paper we are concerned with the challenging problem of producing a full image sequence of a deformable face given only an image and generic facial motions encoded by a set of sparse landmarks. To this end we build upon recent breakthroughs in image-to-image translation such as pix2pix, CycleGAN and StarGAN which learn Deep Convolutional Neural Networks (DCNNs) that learn to map aligned pairs or images between different domains (i.e., having different labels) and propose a new architecture which is not driven any more by labels but by spatial maps, facial landmarks. In particular, we propose the MotionGAN which transforms an input face image into a new one according to a heatmap of target landmarks. We show that it is possible to create very realistic face videos using a single image and a set of target landmarks. Furthermore, our method can be used to edit a facial image with arbitrary motions according to landmarks (e.g., expression, speech, etc.). This provides much more flexibility to face editing, expression transfer, facial video creation, etc. than models based on discrete expressions, audios or action units.

Includes members:
  Member "recycle gan unsupervised video retargeting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "ganimation anatomically aware facial animation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "dual discriminator generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination")
  Member "began boundary equilibrium generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "cvae gan fine grained image generation through asymmetric training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "stargan unified generative adversarial networks for multi domain image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "you said that":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")

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)