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: "a pac bayesian tutorial with a dropout bound"
  Abstract: This tutorial gives a concise overview of existing PAC-Bayesian theory focusing on three generalization bounds. The first is an Occam bound which handles rules with finite precision parameters and which states that generalization loss is near training loss when the number of bits needed to write the rule is small compared to the sample size. The second is a PAC-Bayesian bound providing a generalization guarantee for posterior distributions rather than for individual rules. The PAC-Bayesian bound naturally handles infinite precision rule parameters, $L_2$ regularization, {\em provides a bound for dropout training}, and defines a natural notion of a single distinguished PAC-Bayesian posterior distribution. The third bound is a training-variance bound --- a kind of bias-variance analysis but with bias replaced by expected training loss. The training-variance bound dominates the other bounds but is more difficult to interpret. It seems to suggest variance reduction methods such as bagging and may ultimately provide a more meaningful analysis of dropouts.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "a free energy principle for representation learning"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "therml the thermodynamics of machine learning"
    - "taskonomy disentangling task transfer learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-3 (induced by source paper "on the generalization of bayesian deep nets for multi class classification"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "data dependent stability of stochastic gradient descent"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"

Connected through hyperedge HE-4 (induced by source paper "dropout explicit forms and capacity control"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "on the implicit bias of dropout"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "brain tumor segmentation with deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"

Connected through hyperedge HE-5 (induced by source paper "eternal sunshine of the spotless net selective forgetting in deep networks"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "where is the information in a deep neural network"
    - "knock knock who s there membership inference on aggregate location data"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "logan membership inference attacks against generative models"
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-6 (induced by source paper "fast rate pac bayes generalization bounds via shifted rademacher processes"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "exploring generalization in deep learning"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "a strongly quasiconvex pac bayesian bound"

Connected through hyperedge HE-7 (induced by source paper "where is the information in a deep neural network"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "the information complexity of learning tasks their structure and their distance"
    - "estimating information flow in neural networks"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "train faster generalize better stability of stochastic gradient descent"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-8 (induced by source paper "the information complexity of learning tasks their structure and their distance"):
  with members:
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "taskonomy disentangling task transfer learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "critical learning periods in deep neural networks"
    - "new insights and perspectives on the natural gradient method"
    - "kolmogorov s structure functions and model selection"

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)