human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exploiting smoothness in statistical learning sequential prediction and stochastic optimization"):
  Abstract: In the last several years, the intimate connection between convex optimization and learning problems, in both statistical and sequential frameworks, has shifted the focus of algorithmic machine learning to examine this interplay. In particular, on one hand, this intertwinement brings forward new challenges in reassessment of the performance of learning algorithms including generalization and regret bounds under the assumptions imposed by convexity such as analytical properties of loss functions (e.g., Lipschitzness, strong convexity, and smoothness). On the other hand, emergence of datasets of an unprecedented size, demands the development of novel and more efficient optimization algorithms to tackle large-scale learning problems. #R##N#The overarching goal of this thesis is to reassess the smoothness of loss functions in statistical learning, sequential prediction/online learning, and stochastic optimization and explicate its consequences. In particular we examine how smoothness of loss function could be beneficial or detrimental in these settings in terms of sample complexity, statistical consistency, regret analysis, and convergence rate, and investigate how smoothness can be leveraged to devise more efficient learning algorithms.

Includes members:
  Member "binary excess risk for smooth convex surrogates":
    also connected through other hyperedges:
      (none)
  Member "excess risk bounds for exponentially concave losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion")
  Member "passive learning with target risk":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic approximation of smooth and strongly convex functions beyond the o 1 t convergence rate")
  Member "o logt projections for stochastic optimization of smooth and strongly convex functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic proximal gradient descent for nuclear norm regularization")
  Member "recovering the optimal solution by dual random projection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "hybrid deterministic stochastic methods for data fitting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "on the complexity of bandit and derivative free stochastic convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "naive exploration is optimal for online lqr")
  Member "learning from an optimization viewpoint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "breaking the curse of dimensionality with convex neural networks")

Question: Please tell me which class the source paper 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)