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: "on the quantitative analysis of decoder based generative models"
  Abstract: The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines a generative distribution. Examples include variational autoencoders, generative adversarial networks, and generative moment matching networks. Unfortunately, it can be difficult to quantify the performance of these models because of the intractability of log-likelihood estimation, and inspecting samples can be misleading. We propose to use Annealed Importance Sampling for evaluating log-likelihoods for decoder-based models and validate its accuracy using bidirectional Monte Carlo. The evaluation code is provided at this https URL. Using this technique, we analyze the performance of decoder-based models, the effectiveness of existing log-likelihood estimators, the degree of overfitting, and the degree to which these models miss important modes of the data distribution.

Connected through hyperedge HE-1 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-2 (induced by source paper "scalable approximate inference and some applications"):
  with members:
    - "stein variational inference for discrete distributions"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "deep probabilistic video compression"
    - "a bandit approach to maximum inner product search"
    - "bnn improved binary network training"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "stein variational policy gradient"

Connected through hyperedge HE-3 (induced by source paper "improving image autoencoder embeddings with perceptual loss"):
  with members:
    - "autoencoder based articulatory to acoustic mapping for ultrasound silent speech interfaces"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "recurrent world models facilitate policy evolution"
    - "a pitfall of unsupervised pre training"
    - "perceptual generative adversarial networks for small object detection"
    - "beyond the pixel wise loss for topology aware delineation"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "regularized autoencoders via relaxed injective probability flow"):
  with members:
    - "understanding the un interpretability of natural image distributions using generative models"
    - "diagnosing and enhancing vae models"
    - "from variational to deterministic autoencoders"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "neural discrete representation learning"

Connected through hyperedge HE-5 (induced by source paper "bidirectional generative modeling using adversarial gradient estimation"):
  with members:
    - "large scale adversarial representation learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "pretraining image encoders without reconstruction via feature prediction loss"):
  with members:
    - "improving image autoencoder embeddings with perceptual loss"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "beyond the pixel wise loss for topology aware delineation"
    - "perceptual losses for real time style transfer and super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-7 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-8 (induced by source paper "seeing what a gan cannot generate"):
  with members:
    - "structured gans"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video to video synthesis"
    - "multimodal unsupervised image to image translation"
    - "unified perceptual parsing for scene understanding"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on the implicit assumptions of gans"

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)