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 "time constrained adaptive influence maximization"):
  Abstract: The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process. In its adaptive version, additional seed users can be selected after observing certain diffusion results. On the other hand, social computing tasks are often time-critical, and therefore only the influence resulted in the early period is worthwhile, which can be naturally modeled by enforcing a time constraint. In this paper, we present an analysis of the time-constrained adaptive influence maximization problem. We show that the new problem is combinatorially different from the existing problems, and the current techniques such as submodular maximization and adaptive submodularity are unfortunately inapplicable. On the theory side, we provide the hardness results of computing the optimal policy and a lower bound on the adaptive gap. For practical solutions, from basic to advanced, we design a series of seeding policies for achieving high efficacy and scalability. Finally, we investigate the proposed solutions through extensive simulations based on real-world datasets.

Includes members:
  Member "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of greed influence maximization in undirected networks re visited")
  Member "adaptive influence maximization with myopic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "limitations of greed influence maximization in undirected networks re visited")
  Member "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive sequence submodularity")
  Member "adaptive influence maximization under general feedback models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive influence maximization with myopic feedback")
  Member "on misinformation containment in online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on multi cascade influence maximization model hardness and algorithmic framework")
  Member "multi round influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "target privacy preserving for social networks")
  Member "adaptive submodular influence maximization with myopic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")
  Member "fake news mitigation via point process based intervention":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cluster based social reinforcement 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.SI(Social and Information Networks)