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: "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
  Abstract: We present an algorithm for the statistical learning setting with a bounded exp-concave loss in $d$ dimensions that obtains excess risk $O(d / n)$ with high probability: the dependence on the confidence parameter $\delta$ is polylogarithmic in $1/\delta$. The core technique is to boost the confidence of recent $O(d / n)$ bounds, without sacrificing the rate, by leveraging a Bernstein-type condition which holds due to exp-concavity. This Bernstein-type condition implies that the variance of excess loss random variables are controlled in terms of their excess risk. Using this variance control, we further show that a regret bound for any online learner in this setting translates to a high probability excess risk bound for the corresponding online-to-batch conversion of the online learner.

Connected through hyperedge HE-1 (induced by source paper "efficient improper learning for online logistic regression"):
  with members:
    - "introduction to online convex optimization"
    - "beyond least squares fast rates for regularized empirical risk minimization through self concordance"
    - "logistic regression the importance of being improper"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

Connected through hyperedge HE-2 (induced by source paper "stochastic approximation of smooth and strongly convex functions beyond the o 1 t convergence rate"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "passive learning with target risk"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

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"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-4 (induced by source paper "logistic regression the importance of being improper"):
  with members:
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates in statistical and online learning"
    - "sequential probability assignment with binary alphabets and large classes of experts"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "self concordant analysis for logistic regression"

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:
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "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"

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)