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 "multi round influence maximization"):
  Abstract: In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round. MRIM problem models the viral marketing scenarios in which advertisers conduct multiple rounds of viral marketing to promote one product. We consider two different settings: 1) the non-adaptive MRIM, where the advertiser needs to determine the seed sets for all rounds at the very beginning, and 2) the adaptive MRIM, where the advertiser can select seed sets adaptively based on the propagation results in the previous rounds. For the non-adaptive setting, we design two algorithms that exhibit an interesting tradeoff between efficiency and effectiveness: a cross-round greedy algorithm that selects seeds at a global level and achieves $1/2 - \varepsilon$ approximation ratio, and a within-round greedy algorithm that selects seeds round by round and achieves $1-e^{-(1-1/e)}-\varepsilon \approx 0.46 - \varepsilon$ approximation ratio but saves running time by a factor related to the number of rounds. For the adaptive setting, we design an adaptive algorithm that guarantees $1-e^{-(1-1/e)}-\varepsilon$ approximation to the adaptive optimal solution. In all cases, we further design scalable algorithms based on the reverse influence sampling approach and achieve near-linear running time. We conduct experiments on several real-world networks and demonstrate that our algorithms are effective for the MRIM task.

Includes members:
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "online influence maximization under independent cascade model with semi bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seeding with costly network information")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seeding with costly network information")
  Member "online influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seeding with costly network information")
  Member "from competition to complementarity comparative influence diffusion and maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")
  Member "influence maximization with bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem")
  Member "influence maximization near optimal time complexity meets practical efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time constrained adaptive influence maximization")
  Member "combinatorial multi armed bandit and its extension to probabilistically triggered arms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "combinatorial semi bandit in the non stationary environment")

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.SI(Social and Information Networks)