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: "tightening mutual information based bounds on generalization error"
  Abstract: A mutual information based upper bound on the generalization error of a supervised learning algorithm is derived in this paper. The bound is constructed in terms of the mutual information between each individual training sample and the output of the learning algorithm, which requires weaker conditions on the loss function, but provides a tighter characterization of the generalization error than existing studies. Examples are further provided to demonstrate that the bound derived in this paper is tighter, and has a broader range of applicability. Application to noisy and iterative algorithms, e.g., stochastic gradient Langevin dynamics (SGLD), is also studied, where the constructed bound provides a tighter characterization of the generalization error than existing results.

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"
    - "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"
    - "chaining mutual information and tightening generalization bounds"

Connected through hyperedge HE-2 (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"
    - "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"
    - "chaining mutual information and tightening generalization bounds"
    - "theory iiib generalization in deep networks"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (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"
    - "data dependent pac bayes priors via differential privacy"
    - "generalization error bounds for noisy iterative algorithms"
    - "chaining mutual information and tightening generalization bounds"
    - "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-4 (induced by source paper "gaussian mean field regularizes by limiting learned information"):
  with members:
    - "implicit regularization of stochastic gradient descent in natural language processing observations and implications"
    - "neural ordinary differential equations"
    - "mine mutual information neural estimation"
    - "bounded information rate variational autoencoders"
    - "representation learning with contrastive predictive coding"
    - "parameter space noise for exploration"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-5 (induced by source paper "generalization error bounds via renyi f divergences and maximal leakage"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "an operational approach to information leakage"
    - "chaining mutual information and tightening generalization bounds"
    - "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-6 (induced by source paper "general information bottleneck objectives and their applications to machine learning"):
  with members:
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "variational predictive information bottleneck"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "opening the black box of deep neural networks via information"
    - "maximum mutual information regularized classification"
    - "optimal causal inference estimating stored information and approximating causal architecture"

Connected through hyperedge HE-7 (induced by source paper "enforcing constraints for interpolation and extrapolation in generative adversarial networks"):
  with members:
    - "gaussian mean field regularizes by limiting learned information"
    - "doing the impossible why neural networks can be trained at all"
    - "neural ordinary differential equations"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast and accurate deep network learning by exponential linear units elus"

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)