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: In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding $k$ seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.

Connected through hyperedge HE-1 (induced by source paper "seeding with costly network information"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "metric sublinear algorithms via linear sampling"
    - "the importance of communities for learning to influence"
    - "stochastic submodular maximization the case of coverage functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "almost optimal streaming algorithms for coverage problems"
    - "online influence maximization"
    - "scalable methods for adaptively seeding a social network"

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"
    - "robust influence maximization"
    - "reconstructing parameters of spreading models from partial observations"
    - "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 "diversifying seeds and audience in social influence maximization"):
  with members:
    - "tell me something my friends do not know diversity maximization in social networks"
    - "maximizing the diversity of exposure in a social network"
    - "balancing information exposure in social networks"
    - "node2vec scalable feature learning for networks"
    - "boosting information spread an algorithmic approach"
    - "line large scale information network embedding"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "a k hop collaborate game model adaptive strategy to maximize total revenue"):
  with members:
    - "adaptive influence maximization with myopic feedback"
    - "adaptive influence maximization under general feedback models"
    - "adaptive submodular influence maximization with myopic feedback"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "profit maximization over social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-8 (induced by source paper "adaptive robust optimization with nearly submodular structure"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "robust monotone submodular function maximization"
    - "robust submodular maximization offline and online algorithms"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "deterministic algorithms for submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)