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 "optimization of chance constrained submodular functions"):
  Abstract: Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations based on Chernoff bounds. Furthermore, we investigate the behavior of the algorithms on popular social network problems and show that high quality solutions can still be obtained even if there are strong restrictions imposed by the chance constraint.

Includes members:
  Member "probabilistic tools for the analysis of randomized optimization heuristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "working principles of binary differential evolution")
  Member "evolutionary algorithms for the chance constrained knapsack problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evolutionary bi objective optimization for the dynamic chance constrained knapsack problem based on tail bound objectives")
  Member "submodular maximization beyond non negativity guarantees fast algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularized submodular maximization at scale")
  Member "pareto optimization for subset selection with dynamic cost constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evolutionary bi objective optimization for the dynamic chance constrained knapsack problem based on tail bound objectives")
  Member "greedy maximization of functions with bounded curvature under partition matroid constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non monotone submodular maximization with multiple knapsacks in static and dynamic settings")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "greed is good near optimal submodular maximization via greedy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized submodular maximization at scale")
  Member "social dynamics of digg":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep collaborative embedding for information cascade prediction")

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)