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: "photorealistic facial expression synthesis by the conditional difference adversarial autoencoder"
  Abstract: Photorealistic facial expression synthesis from single face image can be widely applied to face recognition, data augmentation for emotion recognition or entertainment. This problem is challenging, in part due to a paucity of labeled facial expression data, making it difficult for algorithms to disambiguate changes due to identity and changes due to expression. In this paper, we propose the conditional difference adversarial autoencoder, CDAAE, for facial expression synthesis. The CDAAE takes a facial image of a previously unseen person and generates an image of that human face with a target emotion or facial action unit label. The CDAAE adds a feedforward path to an autoencoder structure connecting low level features at the encoder to features at the corresponding level at the decoder. It handles the problem of disambiguating changes due to identity and changes due to facial expression by learning to generate the difference between low-level features of images of the same person but with different facial expressions. The CDAAE structure can be used to generate novel expressions by combining and interpolating between facial expressions/action units within the training set. Our experimental results demonstrate that the CDAAE can preserve identity information when generating facial expression for unseen subjects more faithfully than previous approaches. This is especially advantageous when training with small databases.

Connected through hyperedge HE-1 (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-2 (induced by source paper "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "learning to forecast and refine residual motion for image to video generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "controllable image to video translation a case study on facial expression generation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-3 (induced by source paper "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"):
  with members:
    - "learn to synthesize and synthesize to learn"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-4 (induced by source paper "flnet landmark driven fetching and learning network for faithful talking facial animation synthesis"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "video to video synthesis"
    - "soft gated warping gan for pose guided person image synthesis"
    - "recycle gan unsupervised video retargeting"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"

Connected through hyperedge HE-5 (induced by source paper "conditional adversarial synthesis of 3d facial action units"):
  with members:
    - "recent progress of face image synthesis"
    - "srefi synthesis of realistic example face images"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "exprgan facial expression editing with controllable expression intensity"
    - "multi view image generation from a single view"
    - "age progression regression by conditional adversarial autoencoder"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "deep facial expression recognition a survey"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep covariance descriptors for facial expression recognition"
    - "geometry contrastive gan for facial expression transfer"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "covariance pooling for facial expression recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"

Connected through hyperedge HE-7 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "voice impersonation using generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "face recognition from traditional to deep learning methods"):
  with members:
    - "representation learning by rotating your faces"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

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)