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 for hyperparametric models"):
  Abstract: In this paper, we study the problem of robust influence maximization in the independent cascade model under a hyperparametric assumption. In social networks users influence and are influenced by individuals with similar characteristics and as such, they are associated with some features. A recent surging research direction in influence maximization focuses on the case where the edge probabilities on the graph are not arbitrary but are generated as a function of the features of the users and a global hyperparameter. We propose a model where the objective is to maximize the worst-case number of influenced users for any possible value of that hyperparameter. We provide theoretical results showing that proper robust solution in our model is NP-hard and an algorithm that achieves improper robust optimization. We make-use of sampling based techniques and of the renowned multiplicative weight updates algorithm. Additionally, we validate our method empirically and prove that it outperforms the state-of-the-art robust influence maximization techniques.

Includes members:
  Member "robust optimization for non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distribution agnostic model agnostic meta learning")
  Member "model independent online learning for influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disco influence maximization meets network embedding and deep learning")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seeding with costly network information")
  Member "stability of influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "efficient learning in large scale combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in bandits with knapsacks")
  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 "a data based approach to social influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making")

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.LG(Machine Learning)