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 "competitive influence maximization integrating budget allocation and seed selection"):
  Abstract: Today, many companies take advantage of viral marketing to promote their new products, and since there are several competing companies in many markets, Competitive Influence Maximization has attracted much attention. Two categories of studies exist in the literature. First, studies that focus on which nodes from the network to select considering the existence of the opponents. Second, studies that focus on the problem of budget allocation. In this work, we integrate these two lines of research and propose a new scenario where competition happens in two phases. First, parties identify the most influential nodes of the network. Then they compete over only these influential nodes by the amount of budget they allocate to each node. We provide a strong motivation for this integration. Also, in previous budget allocation studies, any fraction of the budget could be allocated to a node, which means that the action space was continuous. However, in our scenario, we consider that the action space is discrete. This consideration is far more realistic, and it significantly changes the problem. We model the scenario as a game and propose a novel framework for calculating the Nash equilibrium. Notably, building an efficient framework for this problem with such a huge action space and also handling the stochastic environment of influence maximization is very challenging. To tackle these difficulties, we devise a new payoff estimation method and a novel best response oracle to boost the efficiency of our framework. Also, we validate each aspect of our work through extensive experiments.

Includes members:
  Member "advertising competitions in social networks":
    also connected through other hyperedges:
      (none)
  Member "from competition to complementarity comparative influence diffusion and maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")
  Member "strategic resource allocation for competitive influence in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial bandits for sequential learning in colonel blotto games")
  Member "influence maximization near optimal time complexity meets practical efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time constrained adaptive influence maximization")
  Member "maximizing social influence in nearly optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)