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: "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
  Abstract: We show that the average stability notion introduced by \cite{kearns1999algorithmic, bousquet2002stability} is invariant to data preconditioning, for a wide class of generalized linear models that includes most of the known exp-concave losses. In other words, when analyzing the stability rate of a given algorithm, we may assume the optimal preconditioning of the data. This implies that, at least from a statistical perspective, explicit regularization is not required in order to compensate for ill-conditioned data, which stands in contrast to a widely common approach that includes a regularization for analyzing the sample complexity of generalized linear models. Several important implications of our findings include: a) We demonstrate that the excess risk of empirical risk minimization (ERM) is controlled by the preconditioned stability rate. This immediately yields a relatively short and elegant proof for the fast rates attained by ERM in our context. b) We strengthen the recent bounds of \cite{hardt2015train} on the stability rate of the Stochastic Gradient Descent algorithm.

Connected through hyperedge HE-1 (induced by source paper "an exact no free lunch theorem for community detection"):
  with members:
    - "metrics matter in community detection"
    - "evaluation of community structures using kappa index and f score instead of normalized mutual information"
    - "the ground truth about metadata and community detection in networks"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"
    - "estimating network parameters for selecting community detection algorithms"

Connected through hyperedge HE-2 (induced by source paper "metrics matter in community detection"):
  with members:
    - "an exact no free lunch theorem for community detection"
    - "the ground truth about metadata and community detection in networks"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"

Connected through hyperedge HE-3 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "eigenvalue decay implies polynomial time learnability for neural networks"
    - "efficient second order online learning by sketching"
    - "solving ridge regression using sketched preconditioned svrg"
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-4 (induced by source paper "fast rates for empirical risk minimization of strict saddle problems"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "the power of normalization faster evasion of saddle points"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-5 (induced by source paper "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"):
  with members:
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "optimistic rates for learning with a smooth loss"

Connected through hyperedge HE-6 (induced by source paper "solving ridge regression using sketched preconditioned svrg"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "on data preconditioning for regularized loss minimization"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-7 (induced by source paper "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"):
  with members:
    - "fast rates in statistical and online learning"
    - "from stochastic mixability to fast rates"
    - "excess risk bounds for exponentially concave losses"

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)