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 super resolution through wasserstein gans"
  Abstract: Generative adversarial networks (GANs) have received a tremendous amount of attention in the past few years, and have inspired applications addressing a wide range of problems. Despite its great potential, GANs are difficult to train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al. 2017b; and Gulrajani et al. 2017) proposed using Wasserstein distance as the training objective and promised easy, stable GAN training across architectures with minimal hyperparameter tuning. In this paper, we compare the performance of Wasserstein distance with other training objectives on a variety of GAN architectures in the context of single image super-resolution. Our results agree that Wasserstein GAN with gradient penalty (WGAN-GP) provides stable and converging GAN training and that Wasserstein distance is an effective metric to gauge training progress.

Connected through hyperedge HE-1 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "pose guided human video generation"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-2 (induced by source paper "on low resolution face recognition in the wild comparisons and new techniques"):
  with members:
    - "generative adversarial networks an overview"
    - "additive margin softmax for face verification"
    - "deep semantic face deblurring"
    - "deep face deblurring"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-3 (induced by source paper "self enhanced convolutional network for facial video hallucination"):
  with members:
    - "frame recurrent video super resolution"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "residual dense network for image super resolution"
    - "efficient super resolution for large scale images using attentional gan"
    - "non local neural networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-4 (induced by source paper "artist style transfer via quadratic potential"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "gan qp a novel gan framework without gradient vanishing and lipschitz constraint"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "a fully progressive approach to single image super resolution"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-5 (induced by source paper "face recognition in low quality images a survey"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "coupled learning for facial deblur"
    - "super identity convolutional neural network for face hallucination"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "low resolution face recognition"
    - "deep semantic face deblurring"
    - "surveillance face recognition challenge"
    - "s 3 fd single shot scale invariant face detector"

Connected through hyperedge HE-6 (induced by source paper "multimodal sensor fusion in single thermal image super resolution"):
  with members:
    - "densely connected high order residual network for single frame image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "srpgan perceptual generative adversarial network for single image super resolution"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"

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.LG(Machine Learning)