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 "factorization bandits for online influence maximization"):
  Abstract: In this paper, we study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with the network, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including influence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper confidence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online influence maximization algorithm. Extensive empirical evaluations on two real-world networks showed the effectiveness of our proposed solution.

Includes members:
  Member "online influence maximization with local observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial influence maximization")
  Member "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "model independent online learning for influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disco influence maximization meets network embedding and deep learning")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep graph matching consensus")
  Member "online influence maximization under independent cascade model with semi bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeding with costly network information")
  Member "stop and stare optimal sampling algorithms for viral marketing in billion scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time constrained adaptive influence maximization")
  Member "online influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeding with costly network information")
  Member "influence maximization with bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem")

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)