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 "online learning with imperfect hints"):
  Abstract: We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a "hint" vector before choosing the action for that round. Rather surprisingly, it was shown that if the hint vector is guaranteed to have a positive correlation with the cost vector, then the online player can achieve a regret of $O(\log T)$, thus significantly improving over the $O(\sqrt{T})$ regret in the general setting. However, the result and analysis require the correlation property at \emph{all} time steps, thus raising the natural question: can we design online learning algorithms that are resilient to bad hints? #R##N#In this paper we develop algorithms and nearly matching lower bounds for online learning with imperfect directional hints. Our algorithms are oblivious to the quality of the hints, and the regret bounds interpolate between the always-correlated hints case and the no-hints case. Our results also generalize, simplify, and improve upon previous results on optimistic regret bounds, which can be viewed as an additive version of hints.

Includes members:
  Member "adaptive scale invariant online algorithms for learning linear models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms")
  Member "near optimal bounds for online caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online metric algorithms with untrusted predictions")
  Member "parameter free online convex optimization with sub exponential noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive online learning with varying norms")
  Member "matrix free preconditioning in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive online learning with varying norms")
  Member "semi online bipartite matching":
    also connected through other hyperedges:
      (none)
  Member "black box reductions for parameter free online learning in banach spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive online learning with varying norms")
  Member "competitive caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online convex optimization for caching networks")
  Member "online learning sufficient statistics and the burkholder method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive online learning with varying norms")

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)