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 "robust influence maximization"):
  Abstract: Uncertainty about models and data is ubiquitous in the computational social sciences, and it creates a need for robust social network algorithms, which can simultaneously provide guarantees across a spectrum of models and parameter settings. We begin an investigation into this broad domain by studying robust algorithms for the Influence Maximization problem, in which the goal is to identify a set of k nodes in a social network whose joint influence on the network is maximized. #R##N#We define a Robust Influence Maximization framework wherein an algorithm is presented with a set of influence functions, typically derived from different influence models or different parameter settings for the same model. The different parameter settings could be derived from observed cascades on different topics, under different conditions, or at different times. The algorithm's goal is to identify a set of k nodes who are simultaneously influential for all influence functions, compared to the (function-specific) optimum solutions. #R##N#We show strong approximation hardness results for this problem unless the algorithm gets to select at least a logarithmic factor more seeds than the optimum solution. However, when enough extra seeds may be selected, we show that techniques of Krause et al. can be used to approximate the optimum robust influence to within a factor of 1 - 1/e. We evaluate this bicriteria approximation algorithm against natural heuristics on several real-world data sets. Our experiments indicate that the worst-case hardness does not necessarily translate into bad performance on real-world data sets; all algorithms perform fairly well.

Includes members:
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeding with costly network information")
  Member "online influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seeding with costly network information")
  Member "stability of influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "combinatorial multi armed bandit and its extension to probabilistically triggered arms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "trace complexity of network inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "analytical approach to parallel repetition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an improved algorithm for dynamic set cover")
  Member "scalable influence estimation in continuous time diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate k cover in hypergraphs efficient algorithms and applications")

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)