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 continuous submodular maximization from full information to bandit feedback"):
  Abstract: In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from $T^{1/2}$ [Chen2018Online] and $T^{3/2}$ [chen2018projection] to 1, and achieves a $(1-1/e)$-regret bound of $O(T^{4/5})$. The second one, Bandit-Frank-Wolfe, is the first bandit algorithm for continuous DR-submodular maximization, which achieves a $(1-1/e)$-regret bound of $O(T^{8/9})$. Finally, we extend Bandit-Frank-Wolfe to a bandit algorithm for discrete submodular maximization, Responsive-Frank-Wolfe, which attains a $(1-1/e)$-regret bound of $O(T^{8/9})$ in the responsive bandit setting.

Includes members:
  Member "black box submodular maximization discrete and continuous settings":
    also connected through other hyperedges:
      (none)
  Member "optimal dr submodular maximization and applications to provable mean field inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "guaranteed non convex optimization submodular maximization over continuous domains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online policies for efficient volunteer crowdsourcing")
  Member "an optimal algorithm for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs")
  Member "submodular functions from discrete to continous domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "online submodular maximization under a matroid constraint with application to learning assignments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "powerset convolutional neural networks")
  Member "towards minimax policies for online linear optimization with bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret bounds for batched bandits")
  Member "on the complexity of bandit and derivative free stochastic convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "naive exploration is optimal for online lqr")

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)