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: "unsupervised holistic image generation from key local patches"
  Abstract: We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locations of parts at the same time. We construct adversarial networks to tackle this problem. A generator network generates a fake image as well as a mask based on the encoder-decoder framework. On the other hand, a discriminator network aims to detect fake images. The network is trained with three losses to consider spatial, appearance, and adversarial information. The spatial loss determines whether the locations of predicted parts are correct. Input patches are restored in the output image without much modification due to the appearance loss. The adversarial loss ensures output images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on six datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes.

Connected through hyperedge HE-1 (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"
    - "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"
    - "age progression regression by conditional adversarial autoencoder"
    - "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-2 (induced by source paper "generative modeling for small data object detection"):
  with members:
    - "inserting videos into videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "versatile auxiliary classifier with generative adversarial network vac gan multi class scenarios"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"

Connected through hyperedge HE-3 (induced by source paper "disentangled person image generation"):
  with members:
    - "a domain based approach to social relation recognition"
    - "a generative model of people in clothing"
    - "natural and effective obfuscation by head inpainting"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning what and where to draw"

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)