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: "almost everywhere algorithmic stability and generalization error"
  Abstract: We explore in some detail the notion of algorithmic stability as a viable framework for analyzing the generalization error of learning algorithms. We introduce the new notion of training stability of a learning algorithm and show that, in a general setting, it is sufficient for good bounds on generalization error. In the PAC setting, training stability is both necessary and sufficient for learnability.\ The approach based on training stability makes no reference to VC dimension or VC entropy. There is no need to prove uniform convergence, and generalization error is bounded directly via an extended McDiarmid inequality. As a result it potentially allows us to deal with a broader class of learning algorithms than Empirical Risk Minimization. \ We also explore the relationships among VC dimension, generalization error, and various notions of stability. Several examples of learning algorithms are considered.

Connected through hyperedge HE-1 (induced by source paper "making ai forget you data deletion in machine learning"):
  with members:
    - "quantized compressive k means"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "algorithms that remember model inversion attacks and data protection law"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "in datacenter performance analysis of a tensor processing unit"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "fixed point quantization of deep convolutional networks"
    - "training deep neural networks with low precision multiplications"

Connected through hyperedge HE-2 (induced by source paper "stability and optimization error of stochastic gradient descent for pairwise learning"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "data dependent stability of stochastic gradient descent"
    - "robustness and generalization for metric learning"
    - "train faster generalize better stability of stochastic gradient descent"
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "random feature maps for dot product kernels"
    - "learning theory approach to minimum error entropy criterion"

Connected through hyperedge HE-3 (induced by source paper "improvability through semi supervised learning a survey of theoretical results"):
  with members:
    - "the information theoretic value of unlabeled data in semi supervised learning"
    - "when can unlabeled data improve the learning rate"
    - "a distribution dependent and independent complexity analysis of manifold regularization"
    - "on causal and anticausal learning"
    - "active property testing"
    - "transductive rademacher complexity and its applications"
    - "explicit learning curves for transduction and application to clustering and compression algorithms"

Connected through hyperedge HE-4 (induced by source paper "mcdiarmid type inequalities for graph dependent variables and stability bounds"):
  with members:
    - "learning from weakly dependent data under dobrushin s condition"
    - "on learning markov chains"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "learning from networked examples"
    - "chromatic pac bayes bounds for non iid data applications to ranking and stationary beta mixing processes"

Connected through hyperedge HE-5 (induced by source paper "hypothesis set stability and generalization"):
  with members:
    - "generalization bounds for uniformly stable algorithms"
    - "subgaussian tail bounds via stability arguments"
    - "algorithmic stability for adaptive data analysis"
    - "transductive rademacher complexity and its applications"

Connected through hyperedge HE-6 (induced by source paper "gromov hausdorff stability of linkage based hierarchical clustering methods"):
  with members:
    - "a density sensitive hierarchical clustering method"

Connected through hyperedge HE-7 (induced by source paper "learning from an optimization viewpoint"):
  with members:
    - "multiclass learnability and the erm principle"
    - "better mini batch algorithms via accelerated gradient methods"
    - "on the universality of online mirror descent"
    - "a stochastic view of optimal regret through minimax duality"

Connected through hyperedge HE-8 (induced by source paper "geometry of online packing linear programs"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "matroid secretary problem in the random assignment model"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"

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)