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 "scale free online learning"):
  Abstract: We design and analyze algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. Our algorithms are instances of the Follow the Regularized Leader (FTRL) and Mirror Descent (MD) meta-algorithms. We achieve adaptiveness to the norms of the loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. The algorithm based on FTRL works for any decision set, bounded or unbounded. For unbounded decisions sets, this is the first adaptive algorithm for online linear optimization with a non-vacuous regret bound. In contrast, we show lower bounds on scale-free algorithms based on MD on unbounded domains.

Includes members:
  Member "online convex optimization with unconstrained domains and losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "training deep networks without learning rates through coin betting")
  Member "coin betting and parameter free online learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive online learning with varying norms")
  Member "scale free algorithms for online linear optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training neural networks for and by interpolation")
  Member "simultaneous model selection and optimization through parameter free stochastic learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive online learning with varying norms")
  Member "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lipschitz and comparator norm adaptivity in online learning")
  Member "a survey of algorithms and analysis for adaptive online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive online learning with varying norms")
  Member "follow the leader if you can hedge if you must":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast rates for online prediction with abstention")
  Member "optimization learning and games with predictable sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provably efficient adaptive approximate policy iteration")

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)