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 synthesis from visual attributes via sketch using conditional vaes and gans"
  Abstract: Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch. The proposed Attribute2Sketch2Face framework, which is based on a combination of deep Conditional Variational Autoencoder (CVAE) and Generative Adversarial Networks (GANs), consists of three stages: (1) Synthesis of facial sketch from attributes using a CVAE architecture, (2) Enhancement of coarse sketches to produce sharper sketches using a GAN-based framework, and (3) Synthesis of face from sketch using another GAN-based network. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed attribute-based three stage face synthesis method.

Connected through hyperedge HE-1 (induced by source paper "facial synthesis from visual attributes via sketch using multi scale generators"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "on hallucinating context and background pixels from a face mask using multi scale gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "modulating early visual processing by language"
    - "decomposing motion and content for natural video sequence prediction"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-2 (induced by source paper "ftgan a fully trained generative adversarial networks for text to face generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "towards open set identity preserving face synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-3 (induced by source paper "polarimetric thermal to visible face verification via attribute preserved synthesis"):
  with members:
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "thermal to visible synthesis of face images using multiple regions"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-4 (induced by source paper "data driven crowd simulation with generative adversarial networks"):
  with members:
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "unrolled generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "densely connected pyramid dehazing network"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "gated fusion network for single image dehazing"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "residual dense network for image super resolution"
    - "context encoding for semantic segmentation"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-6 (induced by source paper "gp gan gender preserving gan for synthesizing faces from landmarks"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "pose guided person image generation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "natural and effective obfuscation by head inpainting"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "towards realistic face photo sketch synthesis via composition aided gans"):
  with members:
    - "the perception distortion tradeoff"
    - "stylizing face images via multiple exemplars"
    - "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"
    - "focal loss for dense object detection"
    - "random sampling for fast face sketch synthesis"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "in2i unsupervised multi image to image translation using generative adversarial networks"):
  with members:
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning from simulated and unsupervised images through adversarial training"
    - "generative image modeling using style and structure 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)