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: "semi latent gan learning to generate and modify facial images from attributes"
  Abstract: Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of these two problems (generation or manipulation), but not both coherently. This paper proposes a novel model that learns how to both generate and modify the facial image from high-level semantic attributes. Our key idea is to formulate a Semi-Latent Facial Attribute Space (SL-FAS) to systematically learn relationship between user-defined and latent attributes, as well as between those attributes and RGB imagery. As part of this newly formulated space, we propose a new model --- SL-GAN which is a specific form of Generative Adversarial Network. Finally, we present an iterative training algorithm for SL-GAN. The experiments on recent CelebA and CASIA-WebFace datasets validate the effectiveness of our proposed framework. We will also make data, pre-trained models and code available.

Connected through hyperedge HE-1 (induced by source paper "a survey and taxonomy of adversarial neural networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "how generative adversarial networks and their variants work an overview"
    - "mirrorgan learning text to image generation by redescription"
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "adversarial learning of semantic relevance in text to image synthesis"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-2 (induced by source paper "non parametric priors for generative adversarial networks"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "adversarial discriminative domain adaptation"
    - "semantic interpolation in implicit models"

Connected through hyperedge HE-3 (induced by source paper "adjusting decision boundary for class imbalanced learning"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "class balanced loss based on effective number of samples"
    - "imbalanced deep learning by minority class incremental rectification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "optimizing cost sensitive svm for imbalanced data connecting cluster to classification"
    - "focal loss for dense object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "generating photo realistic training data to improve face recognition accuracy"):
  with members:
    - "representation learning by rotating your faces"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "training deep face recognition systems with synthetic data"
    - "privacy protective gan for face de identification"
    - "recent progress of face image synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a large scale attribute dataset for zero shot learning"):
  with members:
    - "zero shot learning the good the bad and the ugly"
    - "invertible conditional gans for image editing"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "zero shot learning posed as a missing data problem"
    - "yolo9000 better faster stronger"
    - "conditional image generation with pixelcnn decoders"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-7 (induced by source paper "editable generative adversarial networks generating and editing faces simultaneously"):
  with members:
    - "high quality bidirectional generative adversarial networks"
    - "mask aware photorealistic face attribute manipulation"
    - "on convergence and stability of gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "triple generative adversarial nets"
    - "style transfer for anime sketches with enhanced residual u net and auxiliary classifier gan"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-8 (induced by source paper "speech driven facial reenactment using conditional generative adversarial networks"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "aenet learning deep audio features for video analysis"
    - "mocogan decomposing motion and content for video generation"
    - "generating images with recurrent adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "generative visual manipulation on the natural image manifold"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "adam a method for stochastic optimization"

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)