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 "marionette few shot face reenactment preserving identity of unseen targets"):
  Abstract: When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting. The identity preservation problem, where the model loses the detailed information of the target leading to a defective output, is the most common failure mode. The problem has several potential sources such as the identity of the driver leaking due to the identity mismatch, or dealing with unseen large poses. To overcome such problems, we introduce components that address the mentioned problem: image attention block, target feature alignment, and landmark transformer. Through attending and warping the relevant features, the proposed architecture, called MarioNETte, produces high-quality reenactments of unseen identities in a few-shot setting. In addition, the landmark transformer dramatically alleviates the identity preservation problem by isolating the expression geometry through landmark disentanglement. Comprehensive experiments are performed to verify that the proposed framework can generate highly realistic faces, outperforming all other baselines, even under a significant mismatch of facial characteristics between the target and the driver.

Includes members:
  Member "progressive pose attention transfer for person image generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "attention based fusion for multi source human image generation":
    also connected through other hyperedges:
      (none)
  Member "few shot adversarial learning of realistic neural talking head models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "opengan open set generative adversarial networks")
  Member "few shot unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "faceswapnet landmark guided many to many face reenactment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lafin generative landmark guided face inpainting")
  Member "cgans with projection discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object centric image generation from layouts")
  Member "soft gated warping gan for pose guided person image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "arbitrary style transfer with style attentional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "swaptext image based texts transfer in scenes")

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)