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 "adversarial influence maximization"):
  Abstract: We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influence process, such that the number of influenced nodes at the conclusion of the campaign is as large as possible. We formulate the problem as a repeated game between a player and adversary, where the adversary specifies the edges along which the contagion may spread, and the player chooses sets of nodes to influence in an online fashion. We establish upper and lower bounds on the minimax pseudo-regret in both undirected and directed networks.

Includes members:
  Member "online influence maximization with local observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factorization bandits for online influence maximization")
  Member "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "activating the breakfast club modeling influence spread in natural world social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "seeding with costly network information")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeding with costly network information")
  Member "learning influence functions from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers")
  Member "online influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeding with costly network information")

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)