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: "semantic facial expression editing using autoencoded flow"
  Abstract: High-level manipulation of facial expressions in images --- such as changing a smile to a neutral expression --- is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.

Connected through hyperedge HE-1 (induced by source paper "joint deep learning of facial expression synthesis and recognition"):
  with members:
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "deep facial expression recognition a survey"
    - "geometry contrastive gan for facial expression transfer"
    - "sphereface deep hypersphere embedding for face recognition"
    - "geometry guided adversarial facial expression synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "region based adversarial synthesis of facial action units"):
  with members:
    - "conditional adversarial synthesis of 3d facial action units"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "deep face recognition a survey"
    - "attention gan for object transfiguration in wild images"
    - "ganimation anatomically aware facial animation from a single image"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-3 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "attribute driven spontaneous motion in unpaired image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "soft gated warping gan for pose guided person image synthesis"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "improving shape deformation in unsupervised image to image translation"
    - "instance level facial attributes transfer with geometry aware flow"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "semi parametric image synthesis"

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 "maskgan towards diverse and interactive facial image manipulation"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "instance level facial attributes transfer with geometry aware flow"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "fine grained head pose estimation without keypoints"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "all in one facial expression transfer editing and recognition using a single network"):
  with members:
    - "explicit facial expression transfer via fine grained semantic representations"
    - "facial expression recognition using human to animated character expression translation"
    - "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets"
    - "facial expression recognition using disentangled adversarial learning"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "geometry guided adversarial facial expression synthesis"
    - "age progression regression by conditional adversarial autoencoder"

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)