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: "uncertainty in the variational information bottleneck"
  Abstract: We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data. Without explicitly being designed to do so, VIB gives two natural metrics for handling and quantifying uncertainty.

Connected through hyperedge HE-1 (induced by source paper "dueling decoders regularizing variational autoencoder latent spaces"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning latent dynamics for planning from pixels"
    - "therml the thermodynamics of machine learning"
    - "group normalization"
    - "the conditional entropy bottleneck"
    - "cross modal deep variational hand pose estimation"
    - "β vaes can retain label information even at high compression"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-2 (induced by source paper "bayesian variational autoencoders for unsupervised out of distribution detection"):
  with members:
    - "a cold approach to generating optimal samples"
    - "conditioning by adaptive sampling for robust design"
    - "all smiles variational autoencoder"
    - "detecting semantic anomalies"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"

Connected through hyperedge HE-3 (induced by source paper "preventing posterior collapse with delta vaes"):
  with members:
    - "stochastic video generation with a learned prior"
    - "autoregressive quantile networks for generative modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "theory and experiments on vector quantized autoencoders"
    - "associative compression networks for representation learning"
    - "generating sentences by editing prototypes"
    - "attention is all you need"
    - "stochastic variational video prediction"

Connected through hyperedge HE-4 (induced by source paper "learning representations for neural network based classification using the information bottleneck principle"):
  with members:
    - "understanding individual neuron importance using information theory"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "i revnet deep invertible networks"
    - "estimating information flow in neural networks"
    - "the variational deficiency bottleneck"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "neural networks regularization through class wise invariant representation learning"

Connected through hyperedge HE-5 (induced by source paper "minimal achievable sufficient statistic learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "estimating information flow in neural networks"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "emergence of invariance and disentanglement in deep representations"
    - "gradient regularization improves accuracy of discriminative models"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "opening the black box of deep neural networks via information"
    - "nonlinear information bottleneck"

Connected through hyperedge HE-6 (induced by source paper "reliable estimation of individual treatment effect with causal information bottleneck"):
  with members:
    - "adversarial balancing based representation learning for causal effect inference with observational data"
    - "improving generalization and stability of generative adversarial networks"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "cycada cycle consistent adversarial domain adaptation"
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "adversarial discriminative domain adaptation"
    - "deep variational information bottleneck"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "input complexity and out of distribution detection with likelihood based generative models"):
  with members:
    - "deep anomaly detection with outlier exposure"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "data discovery and anomaly detection using atypicality theory"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-8 (induced by source paper "unsupervised multi target domain adaptation an information theoretic approach"):
  with members:
    - "task discriminative domain alignment for unsupervised domain adaptation"
    - "unsupervised visual domain adaptation a deep max margin gaussian process approach"
    - "moment matching for multi source domain adaptation"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "mixgan learning concepts from different domains for mixture generation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

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)