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: "density estimation using real nvp"
  Abstract: Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-3 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "graph normalizing flows"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "emerging convolutions for generative normalizing flows"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"

Connected through hyperedge HE-4 (induced by source paper "videoflow a conditional flow based model for stochastic video generation"):
  with members:
    - "waveglow a flow based generative network for speech synthesis"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "fast generation for convolutional autoregressive models"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-5 (induced by source paper "latent normalizing flows for many to many cross domain mappings"):
  with members:
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "preventing posterior collapse with delta vaes"
    - "mirrorgan learning text to image generation by redescription"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "m3d gan multi modal multi domain translation with universal attention"
    - "semantics disentangling for text to image generation"
    - "describing like humans on diversity in image captioning"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"

Connected through hyperedge HE-6 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"

Connected through hyperedge HE-7 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "black box adversarial attacks with limited queries and information"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-8 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"

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)