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: "convolutional network for attribute driven and identity preserving human face generation"
  Abstract: This paper focuses on the problem of generating human face pictures from specific attributes. The existing CNN-based face generation models, however, either ignore the identity of the generated face or fail to preserve the identity of the reference face image. Here we address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image. The attributes can be obtained from the attribute-guided image or by tuning the attribute features of the reference image. With the deep convolutional network "VGG-Face", the loss is defined on the convolutional feature maps. We then apply the gradient decent algorithm to solve this optimization problem. The results validate the effectiveness of our method for attribute driven and identity-preserving face generation.

Connected through hyperedge HE-1 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial vulnerability for any classifier"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "targeted nonlinear adversarial perturbations in images and videos"

Connected through hyperedge HE-2 (induced by source paper "implicit semantic data augmentation for deep networks"):
  with members:
    - "convolutional networks with dense connectivity"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "ensemble soft margin softmax loss for image classification"
    - "autoaugment learning augmentation policies from data"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "attgan facial attribute editing by only changing what you want"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "mask guided portrait editing with conditional gans"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "ganimation anatomically aware facial animation from a single image"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "pose variant 3d facial attribute generation"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "towards open set identity preserving face synthesis"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "fully featured attribute transfer"):
  with members:
    - "modular generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semi supervised fusedgan for conditional image generation"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "towards open set identity preserving face synthesis"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "semantic image synthesis via adversarial learning"
    - "face aging with conditional generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "cvae gan fine grained image generation through asymmetric training"
    - "towards large pose face frontalization in the wild"
    - "stylebank an explicit representation for neural image style transfer"

Connected through hyperedge HE-7 (induced by source paper "self supervised cnn for unconstrained 3d facial performance capture from an rgb d camera"):
  with members:
    - "3d face hallucination from a single depth frame"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "learning dense facial correspondences in unconstrained images"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"

Connected through hyperedge HE-8 (induced by source paper "attgan facial attribute editing by only changing what you want"):
  with members:
    - "dna gan learning disentangled representations from multi attribute images"
    - "combogan unrestrained scalability for image domain translation"
    - "unsupervised image to image translation 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"
    - "unsupervised visual attribute transfer with reconfigurable generative adversarial networks"
    - "fader networks manipulating images by sliding attributes"

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)