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: "refined error bounds for several learning algorithms"
  Abstract: This article studies the achievable guarantees on the error rates of certain learning algorithms, with particular focus on refining logarithmic factors. Many of the results are based on a general technique for obtaining bounds on the error rates of sample-consistent classifiers with monotonic error regions, in the realizable case. We prove bounds of this type expressed in terms of either the VC dimension or the sample compression size. This general technique also enables us to derive several new bounds on the error rates of general sample-consistent learning algorithms, as well as refined bounds on the label complexity of the CAL active learning algorithm. Additionally, we establish a simple necessary and sufficient condition for the existence of a distribution-free bound on the error rates of all sample-consistent learning rules, converging at a rate inversely proportional to the sample size. We also study learning in the presence of classification noise, deriving a new excess error rate guarantee for general VC classes under Tsybakov's noise condition, and establishing a simple and general necessary and sufficient condition for the minimax excess risk under bounded noise to converge at a rate inversely proportional to the sample size.

Connected through hyperedge HE-1 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "divide and conquer local average regression"
    - "distributed learning with regularized least squares"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "optimistic rates for learning with a smooth loss"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fast classification rates without standard margin assumptions"):
  with members:
    - "the relationship between agnostic selective classification active learning and the disagreement coefficient"
    - "when are epsilon nets small"
    - "fast rates in statistical and online learning"

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)