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 multiphase investment strategies for influencing opinions in a social network"):
  Abstract: We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time.

Includes members:
  Member "effectiveness of diffusing information through a social network in multiple phases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a two phase investment game for competitive opinion dynamics in social networks")
  Member "adaptive influence maximization in dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time constrained adaptive influence maximization")
  Member "optimal investment strategies for competing camps in a social network a broad framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks")
  Member "strategic disclosure of opinions on a social network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relaxing exclusive control in boolean games")
  Member "information diffusion in social networks in two phases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "no time to observe adaptive influence maximization with partial feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a k hop collaborate game model adaptive strategy to maximize total revenue")
  Member "a multi phase approach for improving information diffusion in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information diffusion in social networks in two phases")
  Member "scalable methods for adaptively seeding a social network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "time constrained adaptive influence maximization")

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)