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: "rethinking lossy compression the rate distortion perception tradeoff"
  Abstract: Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate. However, in recent years, it has become increasingly accepted that "low distortion" is not a synonym for "high perceptual quality", and in fact optimization of one often comes at the expense of the other. In light of this understanding, it is natural to seek for a generalization of rate-distortion theory which takes perceptual quality into account. In this paper, we adopt the mathematical definition of perceptual quality recently proposed by Blau & Michaeli (2018), and use it to study the three-way tradeoff between rate, distortion, and perception. We show that restricting the perceptual quality to be high, generally leads to an elevation of the rate-distortion curve, thus necessitating a sacrifice in either rate or distortion. We prove several fundamental properties of this triple-tradeoff, calculate it in closed form for a Bernoulli source, and illustrate it visually on a toy MNIST example.

Connected through hyperedge HE-1 (induced by source paper "unsupervised enhancement of real world depth images using tri cycle gan"):
  with members:
    - "s flow gan"
    - "generative adversarial networks for extreme learned image compression"
    - "deep depth completion of a single rgb d image"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-2 (induced by source paper "hierarchical quantized autoencoders"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "on the variance of the adaptive learning rate and beyond"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "the perception distortion tradeoff"
    - "deep generative models for distribution preserving lossy compression"
    - "recent advances in autoencoder based representation learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "variational memory addressing in generative models"

Connected through hyperedge HE-3 (induced by source paper "on mutual information maximization for representation learning"):
  with members:
    - "a theory of usable information under computational constraints"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "on variational bounds of mutual information"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-4 (induced by source paper "information bottleneck through variational glasses"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "deep generative models for distribution preserving lossy compression"
    - "infovae information maximizing variational autoencoders"
    - "fixing a broken elbo"
    - "deep variational information bottleneck"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-5 (induced by source paper "the perception distortion tradeoff"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "rate distortion perception tradeoff of variable length source coding for general information sources"
    - "generative adversarial networks for extreme learned image compression"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "introducing the perception distortion tradeoff into the rate distortion theory of general information sources"
    - "deep generative models for distribution preserving lossy compression"

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)