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 "a two phase investment game for competitive opinion dynamics in social networks"):
  Abstract: We propose a setting for two-phase opinion dynamics in social networks, where a node's final opinion in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions, by strategically investing on nodes in the two phases. A node's initial opinion in the second phase naturally plays a key role in determining the final opinion of that node, and hence also of other nodes in the network due to its influence on them. More importantly, this bias also determines the effectiveness of a camp's investment on that node in the second phase. To formalize this two-phase investment setting, we propose an extension of Friedkin-Johnsen model, and hence formulate the utility functions of the camps. There is a tradeoff while splitting the budget between the two phases. A lower investment in the first phase results in worse initial biases for the second phase, while a higher investment spares a lower available budget for the second phase. We first analyze the non-competitive case where only one camp invests, for which we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. We then analyze the case of competing camps, where we show the existence of Nash equilibrium and that it can be computed in polynomial time under reasonable assumptions. We conclude our study with simulations on real-world network datasets, in order to quantify the effects of the initial biases and the weightage attributed by nodes to their initial biases, as well as that of a camp deviating from its equilibrium strategy. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to effectively influence the biases to be harnessed in the second phase.

Includes members:
  Member "an integrated framework for competitive multi channel marketing of multi featured products":
    also connected through other hyperedges:
      (none)
  Member "multi round influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time constrained adaptive influence maximization")
  Member "effectiveness of diffusing information through a social network in multiple phases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal multiphase investment strategies for influencing opinions in a social network")
  Member "optimal multiphase investment strategies for influencing opinions in a social network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effectiveness of diffusing information through a social network in multiple phases")
  Member "adaptive influence maximization in dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks")
  Member "opinion dynamics in social networks with hostile camps consensus vs polarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "percolation threshold for competitive influence in random networks")
  Member "information diffusion in social networks in two phases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")

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)