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 "optimal anytime regret with two experts"):
  Abstract: The multiplicative weights method is an algorithm for the problem of prediction with expert advice. It achieves the minimax regret asymptotically if the number of experts is large, and the time horizon is known in advance. Optimal algorithms are also known if there are exactly two or three experts, and the time horizon is known in advance. #R##N#In the anytime setting, where the time horizon is not known in advance, algorithms can be obtained by the doubling trick, but they are not optimal, let alone practical. No minimax optimal algorithm was previously known in the anytime setting, regardless of the number of experts. #R##N#We design the first minimax optimal algorithm for minimizing regret in the anytime setting. We consider the case of two experts, and prove that the optimal regret is $\gamma \sqrt{t} / 2$ at all time steps $t$, where $\gamma$ is a natural constant that arose 35 years ago in studying fundamental properties of Brownian motion. The algorithm is designed by considering a continuous analogue, which is solved using ideas from stochastic calculus.

Includes members:
  Member "krivine diffusions attain the goemans williamson approximation ratio":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sticky brownian rounding and its applications to constraint satisfaction problems")
  Member "sticky brownian rounding and its applications to constraint satisfaction problems":
    also connected through other hyperedges:
      (none)
  Member "tight lower bounds for multiplicative weights algorithmic families":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial policies in learning systems with malicious experts")
  Member "geodesic walks in polytopes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the approximability of weighted model integration on dnf structures")
  Member "towards optimal algorithms for prediction with expert advice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial policies in learning systems with malicious experts")
  Member "towards minimax online learning with unknown time horizon":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an approximate dynamic programming approach to repeated games with vector losses")

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)