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: "vgan based image representation learning for privacy preserving facial expression recognition"
  Abstract: Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.

Connected through hyperedge HE-1 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "improving person re identification by attribute and identity learning"
    - "deep facial expression recognition a survey"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "low shot learning from imaginary data"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"

Connected through hyperedge HE-2 (induced by source paper "a cyclically trained adversarial network for invariant representation learning"):
  with members:
    - "a two step disentanglement method"
    - "domain generalization via conditional invariant representation"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "controllable invariance through adversarial feature learning"
    - "fader networks manipulating images by sliding attributes"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "evaluating the effectiveness of automated identity masking aim methods with human perception and a deep convolutional neural network cnn"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "the do s and don ts for cnn based face verification"
    - "face deidentification with generative deep neural networks"
    - "triplet probabilistic embedding for face verification and clustering"
    - "deep residual learning for image recognition"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-4 (induced by source paper "vae wgan based image representation learning for pose preserving seamless identity replacement in facial images"):
  with members:
    - "controllable invariance through adversarial feature learning"
    - "improved training of wasserstein gans"
    - "censoring representations with an adversary"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (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"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"
    - "relaxed spatio temporal deep feature aggregation for real fake expression prediction"

Connected through hyperedge HE-6 (induced by source paper "optimal utility privacy trade off with the total variation distance as the privacy measure"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "the utility cost of robust privacy guarantees"
    - "a tunable measure for information leakage"
    - "privacy preserving adversarial networks"
    - "privacy aware guessing efficiency"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-7 (induced by source paper "siamese generative adversarial privatizer for biometric data"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "privacy preserving adversarial networks"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "context aware generative adversarial privacy"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "adversarial image perturbation for privacy protection a game theory perspective"

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)