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 aging with conditional generative adversarial networks"
  Abstract: It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for "Identity-Preserving" optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "rank consistent ordinal regression for neural networks"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "right for the right reason training agnostic networks"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"

Connected through hyperedge HE-3 (induced by source paper "dp cgan differentially private synthetic data and label generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating and designing dna with deep generative models"
    - "logan membership inference attacks against generative models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "understanding deep learning requires rethinking generalization"
    - "concentrated differential privacy"

Connected through hyperedge HE-4 (induced by source paper "child face age progression via deep feature aging"):
  with members:
    - "decorrelated adversarial learning for age invariant face recognition"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "learning face age progression a pyramid architecture of gans"

Connected through hyperedge HE-5 (induced by source paper "interpreting galaxy deblender gan from the discriminator s perspective"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "beyond saliency understanding convolutional neural networks from saliency prediction on layer wise relevance propagation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-6 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-7 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "pose guided human video generation"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-8 (induced by source paper "dual encoder decoder based generative adversarial networks for disentangled facial representation learning"):
  with members:
    - "representation learning by rotating your faces"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "attgan facial attribute editing by only changing what you want"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

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)