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: "chaining mutual information and tightening generalization bounds"
  Abstract: Bounding the generalization error of learning algorithms has a long history, which yet falls short in explaining various generalization successes including those of deep learning. Two important difficulties are (i) exploiting the dependencies between the hypotheses, (ii) exploiting the dependence between the algorithm's input and output. Progress on the first point was made with the chaining method, originating from the work of Kolmogorov, and used in the VC-dimension bound. More recently, progress on the second point was made with the mutual information method by Russo and Zou '15. Yet, these two methods are currently disjoint. In this paper, we introduce a technique to combine the chaining and mutual information methods, to obtain a generalization bound that is both algorithm-dependent and that exploits the dependencies between the hypotheses. We provide an example in which our bound significantly outperforms both the chaining and the mutual information bounds. As a corollary, we tighten Dudley's inequality when the learning algorithm chooses its output from a small subset of hypotheses with high probability.

Connected through hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information"):
  with members:
    - "robust generalization via α mutual information"
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "strengthened information theoretic bounds on the generalization error"
    - "tightening mutual information based bounds on generalization error"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "a new analysis of differential privacy s generalization guarantees"
    - "pac learning with stable and private predictions"
    - "a direct sum result for the information complexity of learning"

Connected through hyperedge HE-2 (induced by source paper "norm based generalisation bounds for multi class convolutional neural networks"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "data dependent pac bayes priors via differential privacy"

Connected through hyperedge HE-3 (induced by source paper "think global act local relating dnn generalisation and node level snr"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "tightening mutual information based bounds on generalization error"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "on variational bounds of mutual information"
    - "entropy and mutual information in models of deep neural networks"
    - "putting an end to end to end gradient isolated learning of representations"
    - "theory iiib generalization in deep networks"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "margin maximization as lossless maximal compression"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "opening the black box of deep neural networks via information"
    - "a brief introduction to machine learning for engineers"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "information bottleneck and its applications in deep learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "mathematics of deep learning"
    - "infovae information maximizing variational autoencoders"
    - "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"
    - "sharp minima can generalize for deep nets"
    - "learners that use little information"

Connected through hyperedge HE-6 (induced by source paper "chaining meets chain rule multilevel entropic regularization and training of neural nets"):
  with members:
    - "distribution dependent analysis of gibbs erm principle"
    - "tightening mutual information based bounds on generalization error"
    - "data dependent pac bayes priors via differential privacy"
    - "generalization error bounds for noisy iterative algorithms"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "generalization error bounds via renyi f divergences and maximal leakage"):
  with members:
    - "tightening mutual information based bounds on generalization error"
    - "generalization error bounds for noisy iterative algorithms"
    - "an operational approach to information leakage"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "learners that use little information"
    - "dependence measures bounding the exploration bias for general measurements"
    - "max information differential privacy and post selection hypothesis testing"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-8 (induced by source paper "improved generalisation bounds for deep learning through l covering numbers"):
  with members:
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a style based generator architecture for generative adversarial networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "on generalization bounds of a family of recurrent 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"

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)