human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "face transfer with generative adversarial network"
  Abstract: Face transfer animates the facial performances of the character in the target video by a source actor. Traditional methods are typically based on face modeling. We propose an end-to-end face transfer method based on Generative Adversarial Network. Specifically, we leverage CycleGAN to generate the face image of the target character with the corresponding head pose and facial expression of the source. In order to improve the quality of generated videos, we adopt PatchGAN and explore the effect of different receptive field sizes on generated images.

Connected through hyperedge HE-1 (induced by source paper "dual generator generative adversarial networks for multi domain image to image translation"):
  with members:
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "every smile is unique landmark guided diverse smile generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "generative face completion"

Connected through hyperedge HE-2 (induced by source paper "video to video translation for visual speech synthesis"):
  with members:
    - "video to video synthesis"
    - "artistic style transfer for videos and spherical images"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "multimodal unsupervised image to image translation"
    - "to create what you tell generating videos from captions"
    - "ganimation anatomically aware facial animation from a single image"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-3 (induced by source paper "marionette few shot face reenactment preserving identity of unseen targets"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "attention based fusion for multi source human image generation"
    - "few shot adversarial learning of realistic neural talking head models"
    - "few shot unsupervised image to image translation"
    - "faceswapnet landmark guided many to many face reenactment"
    - "cgans with projection discriminator"
    - "soft gated warping gan for pose guided person image synthesis"
    - "arbitrary style transfer with style attentional networks"

Connected through hyperedge HE-4 (induced by source paper "a neural virtual anchor synthesizer based on seq2seq and gan models"):
  with members:
    - "video synthesis of human upper body with realistic face"
    - "recycle gan unsupervised video retargeting"
    - "deep contextualized word representations"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "everybody dance now"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "one shot face reenactment"):
  with members:
    - "dense intrinsic appearance flow for human pose transfer"
    - "semantic image synthesis with spatially adaptive normalization"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "facelet bank for fast portrait manipulation"
    - "synthesizing images of humans in unseen poses"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "a variational u net for conditional appearance and shape generation"
    - "instance level facial attributes transfer with geometry aware flow"

Connected through hyperedge HE-6 (induced by source paper "icface interpretable and controllable face reenactment using gans"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "facelet bank for fast portrait manipulation"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "cr gan learning complete representations for multi view generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "ganimation anatomically aware facial animation from a single image"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-7 (induced by source paper "fakecatcher detection of synthetic portrait videos using biological signals"):
  with members:
    - "video to video synthesis"
    - "mesonet a compact facial video forgery detection network"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "ganimation anatomically aware facial animation from a single image"
    - "detection of deep network generated images using disparities in color components"
    - "deepphys video based physiological measurement using convolutional attention networks"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-8 (induced by source paper "faceswapnet landmark guided many to many face reenactment"):
  with members:
    - "pfld a practical facial landmark detector"
    - "look at boundary a boundary aware face alignment algorithm"
    - "soft gated warping gan for pose guided person image synthesis"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "style aggregated network for facial landmark detection"
    - "a style based generator architecture for generative adversarial networks"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Question: Please tell me which class the center node 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)