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 "explicit facial expression transfer via fine grained semantic representations"):
  Abstract: Facial expression transfer between two unpaired images is a challenging problem, as fine-grained expressions are typically tangled with other facial attributes such as identity and pose. Most existing methods treat expression transfer as an application of expression manipulation, and use predicted facial expressions, landmarks or action units (AUs) of a source image to guide the expression edit of a target image. However, the prediction of expressions, landmarks and especially AUs may be inaccurate, which limits the accuracy of transferring fine-grained expressions. Instead of using an intermediate estimated guidance, we propose to explicitly transfer expressions by directly mapping two unpaired images to two synthesized images with swapped expressions. Since each AU semantically describes local expression details, we can synthesize new images with preserved identities and swapped expressions by combining AU-free features with swapped AU-related features. To disentangle the images into AU-related features and AU-free features, we propose a novel adversarial training method which can solve the adversarial learning of multi-class classification problems. Moreover, to obtain reliable expression transfer results of the unpaired input, we introduce a swap consistency loss to make the synthesized images and self-reconstructed images indistinguishable. Extensive experiments on RaFD, MMI and CFD datasets show that our approach can generate photo-realistic expression transfer results between unpaired images with different expression appearances including genders, ages, races and poses.

Includes members:
  Member "pluralistic image completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "deep adaptive attention for joint facial action unit detection and face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "region based adversarial synthesis of facial action units")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "ganimation anatomically aware facial animation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "drit diverse image to image translation via disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "geometry guided adversarial facial expression synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "exprgan facial expression editing with controllable expression intensity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inducing optimal attribute representations for conditional gans")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    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)