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 "non monotone submodular maximization with multiple knapsacks in static and dynamic settings"):
  Abstract: We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints. We propose a simple discrete greedy algorithm to approach this problem, and prove that it yields strong approximation guarantees for functions with bounded curvature. In contrast to other heuristics, this requires no problem relaxation to continuous domains and it maintains a constant-factor approximation guarantee in the problem size. In the case of a single knapsack, our analysis suggests that the standard greedy can be used in non-monotone settings. #R##N#Additionally, we study this problem in a dynamic setting, by which knapsacks change during the optimization process. We modify our greedy algorithm to avoid a complete restart at each constraint update. This modification retains the approximation guarantees of the static case. #R##N#We evaluate our results experimentally on a video summarization and sensor placement task. We show that our proposed algorithm competes with the state-of-the-art in static settings. Furthermore, we show that in dynamic settings with tight computational time budget, our modified greedy yields significant improvements over starting the greedy from scratch, in terms of the solution quality achieved.

Includes members:
  Member "optimization of chance constrained submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evolutionary bi objective optimization for the dynamic chance constrained knapsack problem based on tail bound objectives")
  Member "pareto optimization for subset selection with dynamic cost constraints":
    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 "greedy maximization of functions with bounded curvature under partition matroid constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimization of chance constrained submodular functions")
  Member "streaming non monotone submodular maximization personalized video summarization on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "optimal approximation for submodular and supermodular optimization with bounded curvature":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized submodular maximization at scale")
  Member "max sum diversification monotone submodular functions and dynamic updates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential evaluation and generation framework for combinatorial recommender system")
  Member "maximizing non monotone submodular set functions subject to different constraints combined algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "submodular function maximization via the multilinear relaxation and contention resolution schemes")

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.AI(Artificial Intelligence)