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: "detecting overfitting of deep generative networks via latent recovery"
  Abstract: State of the art deep generative networks are capable of producing images with such incredible realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We demonstrate this is not sufficient and motivates the need to study memorization/overfitting of deep generators with more scrutiny. This paper addresses this question by i) showing how simple losses are highly effective at reconstructing images for deep generators ii) analyzing the statistics of reconstruction errors when reconstructing training and validation images, which is the standard way to analyze overfitting in machine learning. Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods. The paper also shows that standard GAN evaluation metrics fail to capture memorization for some deep generators. Finally, the paper also shows how off-the-shelf GAN generators can be successfully applied to face inpainting and face super-resolution using the proposed reconstruction method, without hybrid adversarial losses.

Connected through hyperedge HE-1 (induced by source paper "dynamic multi object gaussian process models a framework for data driven functional modelling of human joints"):
  with members:
    - "a closest point proposal for mcmc based probabilistic surface registration"
    - "densely connected convolutional networks"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "image2stylegan how to edit the embedded images"):
  with members:
    - "seeing what a gan cannot generate"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "few shot video to video synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "revisiting precision and recall definition for generative model evaluation"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "large scale gan training for high fidelity natural image synthesis"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "segan speech enhancement generative adversarial network"
    - "word translation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "source generator attribution via inversion"):
  with members:
    - "do gans leave artificial fingerprints"
    - "learning to detect fake face images in the wild"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "spectral normalization for generative adversarial networks"
    - "learning inverse mapping by autoencoder based generative adversarial nets"
    - "progressive growing of gans for improved quality stability and variation"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "generative visual manipulation on the natural image manifold"

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)