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 "simultaneous model selection and optimization through parameter free stochastic learning"):
  Abstract: Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been proposed to speed up their convergence, the model selection phase is often ignored. In fact, in theoretical works most of the time assumptions are made, for example, on the prior knowledge of the norm of the optimal solution, while in the practical world validation methods remain the only viable approach. In this paper, we propose a new kernel-based stochastic gradient descent algorithm that performs model selection while training, with no parameters to tune, nor any form of cross-validation. The algorithm builds on recent advancement in online learning theory for unconstrained settings, to estimate over time the right regularization in a data-dependent way. Optimal rates of convergence are proved under standard smoothness assumptions on the target function, using the range space of the fractional integral operator associated with the kernel.

Includes members:
  Member "a drifting games analysis for online learning and applications to boosting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online influence maximization with local observations")
  Member "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lipschitz and comparator norm adaptivity in online learning")
  Member "non strongly convex smooth stochastic approximation with convergence rate o 1 n":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "no regret algorithms for unconstrained online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive online learning with varying norms")
  Member "less regret via online conditioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a modern introduction to online learning")
  Member "a parameter free hedging algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a modern introduction to online learning")

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)