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 analysis of randomized learning with application to stochastic gradient descent"
  Abstract: We study the generalization error of randomized learning algorithms -- focusing on stochastic gradient descent (SGD) -- using a novel combination of PAC-Bayes and algorithmic stability. Importantly, our generalization bounds hold for all posterior distributions on an algorithm's random hyperparameters, including distributions that depend on the training data. This inspires an adaptive sampling algorithm for SGD that optimizes the posterior at runtime. We analyze this algorithm in the context of our generalization bounds and evaluate it on a benchmark dataset. Our experiments demonstrate that adaptive sampling can reduce empirical risk faster than uniform sampling while also improving out-of-sample accuracy.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on generalization error bounds of noisy gradient methods for non convex learning"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "on exact computation with an infinitely wide neural net"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "generalization error bounds for noisy iterative algorithms"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"):
  with members:
    - "hypothesis set stability and generalization"
    - "generalization bounds for uniformly stable algorithms"
    - "privacy preserving prediction"
    - "tight analyses for non smooth stochastic gradient descent"
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "data dependent stability of stochastic gradient descent"
    - "subgaussian tail bounds via stability arguments"
    - "concentration bounds for high sensitivity functions through differential privacy"

Connected through hyperedge HE-4 (induced by source paper "time delay momentum a regularization perspective on the convergence and generalization of stochastic momentum for deep learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "a unified analysis of stochastic momentum methods for deep learning"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "decoupled weight decay regularization"
    - "data dependent stability of stochastic gradient descent"
    - "eve a gradient based optimization method with locally and globally adaptive learning rates"

Connected through hyperedge HE-5 (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 bayesian perspective on generalization and stochastic gradient descent"
    - "a strongly quasiconvex pac bayesian bound"
    - "fast rates in statistical and online learning"

Connected through hyperedge HE-6 (induced by source paper "pac bayes with backprop"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "exploring generalization in deep learning"
    - "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 strongly quasiconvex pac bayesian bound"
    - "a note on the pac bayesian theorem"

Connected through hyperedge HE-7 (induced by source paper "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"):
  with members:
    - "deep sets"
    - "saliency based sequential image attention with multiset prediction"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "deep supervised discrete hashing"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "structured generative adversarial networks"
    - "first order adaptive sample size methods to reduce complexity of empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "data dependent pac bayes priors via differential privacy"):
  with members:
    - "exploring generalization in deep learning"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "spectrally normalized margin bounds for neural networks"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"

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)