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: "age progression regression by conditional adversarial autoencoder"
  Abstract: If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a "No." Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.

Connected through hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision"):
  with members:
    - "a two step disentanglement method"
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "diversity in faces"
    - "toward gender inclusive coreference resolution"
    - "robust ordinal vae employing noisy pairwise comparisons for disentanglement"
    - "weakly supervised disentanglement with guarantees"
    - "contrastive variational autoencoder enhances salient features"
    - "weakly supervised disentanglement by pairwise similarities"

Connected through hyperedge HE-2 (induced by source paper "stylegan2 distillation for feed forward image manipulation"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "analyzing and improving the image quality of stylegan"
    - "learning continuous face age progression a pyramid of gans"
    - "face to parameter translation for game character auto creation"
    - "meta sim learning to generate synthetic datasets"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "classification accuracy score for conditional generative models"

Connected through hyperedge HE-3 (induced by source paper "inducing optimal attribute representations for conditional gans"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "multi label image recognition with graph convolutional networks"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "auglabel exploiting word representations to augment labels for face attribute classification"
    - "biphasic learning of gans for high resolution image to image translation"
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-4 (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-5 (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-6 (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"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"

Connected through hyperedge HE-7 (induced by source paper "controllable descendant face synthesis"):
  with members:
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "kinshipgan synthesizing of kinship faces from family photos by regularizing a deep face network"
    - "unsupervised holistic image generation from key local patches"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "makeup like a superstar deep localized makeup transfer network"

Connected through hyperedge HE-8 (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"
    - "face aging with conditional generative adversarial networks"

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)