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: "hybrid models with deep and invertible features"
  Abstract: We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning.

Connected through hyperedge HE-1 (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"
    - "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"
    - "autoregressive quantile networks for generative modeling"

Connected through hyperedge HE-2 (induced by source paper "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"):
  with members:
    - "conditional generative models are not robust"
    - "hexagan generative adversarial nets for real world classification"
    - "sum of squares polynomial flow"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "guided image generation with conditional invertible neural networks"
    - "invertible residual networks"
    - "excessive invariance causes adversarial vulnerability"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-3 (induced by source paper "understanding the limitations of conditional generative models"):
  with members:
    - "semi supervised learning with normalizing flows"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-4 (induced by source paper "simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network"):
  with members:
    - "connections between support vector machines wasserstein distance and gradient penalty gans"
    - "i revnet deep invertible networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "neural discrete representation learning"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-5 (induced by source paper "a linear systems theory of normalizing flows"):
  with members:
    - "emerging convolutions for generative normalizing flows"
    - "loss landscapes of regularized linear autoencoders"
    - "density estimation using real nvp"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-6 (induced by source paper "semi supervised learning with normalizing flows"):
  with members:
    - "mintnet building invertible neural networks with masked convolutions"
    - "unsupervised data augmentation for consistency training"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "invertible residual networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "infocnf an efficient conditional continuous normalizing flow with adaptive solvers"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "latent odes for irregularly sampled time series"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "analyzing inverse problems with invertible neural networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "skipnet learning dynamic routing in convolutional networks"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-8 (induced by source paper "out of distribution detection using neural rendering generative models"):
  with members:
    - "efficient gan based anomaly detection"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "neural rendering model joint generation and prediction for semi supervised learning"
    - "on calibration of modern neural networks"
    - "striving for simplicity the all convolutional net"
    - "deep learning face attributes in the wild"
    - "visualizing and understanding convolutional networks"

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)