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: "importance weighted autoencoders"
  Abstract: The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately factorial, and that its parameters can be approximated with nonlinear regression from the observations. As we show empirically, the VAE objective can lead to overly simplified representations which fail to use the network's entire modeling capacity. We present the importance weighted autoencoder (IWAE), a generative model with the same architecture as the VAE, but which uses a strictly tighter log-likelihood lower bound derived from importance weighting. In the IWAE, the recognition network uses multiple samples to approximate the posterior, giving it increased flexibility to model complex posteriors which do not fit the VAE modeling assumptions. We show empirically that IWAEs learn richer latent space representations than VAEs, leading to improved test log-likelihood on density estimation benchmarks.

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"
    - "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-2 (induced by source paper "a neural dirichlet process mixture model for task free continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "on tiny episodic memories in continual learning"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "reconciling meta learning and continual learning with online mixtures of tasks"
    - "continuous learning in single incremental task scenarios"

Connected through hyperedge HE-3 (induced by source paper "ae ot gan training gans from data specific latent distribution"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "diagnosing and enhancing vae models"
    - "large scale adversarial representation learning"
    - "generating diverse high fidelity images with vq vae 2"
    - "disconnected manifold learning for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder"):
  with members:
    - "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"
    - "time series anomaly detection with variational autoencoders"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "unsupervised out of distribution detection with batch normalization"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"

Connected through hyperedge HE-5 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "fuseseg lidar point cloud segmentation fusing multi modal data"
    - "sensor fusion using backward shortcut connections for sleep apnea detection in multi modal data"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "atloc attention guided camera localization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-6 (induced by source paper "closing the dequantization gap pixelcnn as a single layer flow"):
  with members:
    - "learning discrete distributions by dequantization"
    - "integer discrete flows and lossless compression"
    - "sum of squares polynomial flow"
    - "discrete flows invertible generative models of discrete data"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-7 (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-8 (induced by source paper "predictive coding for locally linear control"):
  with members:
    - "predictive coding for boosting deep reinforcement learning with sparse rewards"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "on variational bounds of mutual information"
    - "prediction consistency curvature representation learning for locally linear control"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "learning latent dynamics for planning from pixels"
    - "near optimal representation learning for hierarchical reinforcement learning"

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)