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 "fast rates for online prediction with abstention"):
  Abstract: In the setting of sequential prediction of individual $\{0, 1\}$-sequences with expert advice, we show that by allowing the learner to abstain from the prediction by paying a cost marginally smaller than $\frac 12$ (say, $0.49$), it is possible to achieve expected regret bounds that are independent of the time horizon $T$. We exactly characterize the dependence on the abstention cost $c$ and the number of experts $N$ by providing matching upper and lower bounds of order $\frac{\log N}{1-2c}$, which is to be contrasted with the best possible rate of $\sqrt{T\log N}$ that is available without the option to abstain. We also discuss various extensions of our model, including a setting where the sequence of abstention costs can change arbitrarily over time, where we show regret bounds interpolating between the slow and the fast rates mentioned above, under some natural assumptions on the sequence of abstention costs.

Includes members:
  Member "fast classification rates without standard margin assumptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploratory machine learning with unknown unknowns")
  Member "online learning with abstention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selective brain damage measuring the disparate impact of model pruning")
  Member "combining adversarial guarantees and stochastic fast rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lipschitz adaptivity with multiple learning rates in online learning")
  Member "the extended littlestone s dimension for learning with mistakes and abstentions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online learning with abstention")
  Member "fast rates in statistical and online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "the sample complexity of learning linear predictors with the squared loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "functional transparency for structured data a game theoretic approach")
  Member "follow the leader if you can hedge if you must":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lipschitz adaptivity with multiple learning rates in 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)