human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"

Connected through hyperedge HE-2 (induced by source paper "scalable influence estimation without sampling"):
  with members:
    - "cascade size distributions and why they matter"
    - "outward influence and cascade size estimation in billion scale networks"
    - "nonbacktracking bounds on the influence in independent cascade models"
    - "optimal deployment of resources for maximizing impact in spreading processes"
    - "reconstructing parameters of spreading models from partial observations"
    - "robust influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-3 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries"):
  with members:
    - "robust densest subgraph discovery"
    - "on finding dense common subgraphs"
    - "twawler a lightweight twitter crawler"
    - "risk averse matchings over uncertain graph databases"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "robust influence maximization"
    - "best friends forever bff finding lasting dense subgraphs"
    - "streaming graph partitioning in the planted partition model"

Connected through hyperedge HE-4 (induced by source paper "influence maximization with spontaneous user adoption"):
  with members:
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "multi round influence maximization"
    - "robust influence maximization"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-5 (induced by source paper "robust influence maximization for hyperparametric models"):
  with members:
    - "robust optimization for non convex objectives"
    - "model independent online learning for influence maximization"
    - "robust influence maximization"
    - "stability of influence maximization"
    - "efficient learning in large scale combinatorial semi bandits"
    - "analytical approach to parallel repetition"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-6 (induced by source paper "running time analysis of the 1 1 ea for robust linear optimization"):
  with members:
    - "robust monotone submodular function maximization"
    - "multi objective maximization of monotone submodular functions with cardinality constraint"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "analyzing evolutionary optimization in noisy environments"
    - "multiplicative drift analysis"

Connected through hyperedge HE-7 (induced by source paper "beyond worst case in approximability of nonsubmodular influence maximization"):
  with members:
    - "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"
    - "robust influence maximization"
    - "competitive contagion in networks"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-8 (induced by source paper "multi round influence maximization"):
  with members:
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "online influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "maximizing social influence in nearly optimal time"

Question: Please tell me which class the center node 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)