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 "on maximizing a monotone k submodular function subject to a matroid constraint"):
  Abstract: A $k$-submodular function is an extension of a submodular function in that its input is given by $k$ disjoint subsets instead of a single subset. For unconstrained nonnegative $k$-submodular maximization, Ward and \v{Z}ivn\'y proposed a constant-factor approximation algorithm, which was improved by the recent work of Iwata, Tanigawa and Yoshida presenting a $1/2$-approximation algorithm. Iwata et al. also provided a $k/(2k-1)$-approximation algorithm for monotone $k$-submodular maximization and proved that its approximation ratio is asymptotically tight. More recently, Ohsaka and Yoshida proposed constant-factor algorithms for monotone $k$-submodular maximization with several size constraints. However, while submodular maximization with various constraints has been extensively studied, no approximation algorithm has been developed for constrained $k$-submodular maximization, except for the case of size constraints. In this paper, we prove that a greedy algorithm outputs a $1/2$-approximate solution for monotone $k$-submodular maximization with a matroid constraint. The algorithm runs in $O(M|E|(\text{MO} + k\text{EO}))$ time, where $M$ is the size of a maximal optimal solution, $|E|$ is the size of the ground set, and $\text{MO}, \text{EO}$ represent the time for the membership oracle of the matroid and the evaluation oracle of the $k$-submodular function, respectively.

Includes members:
  Member "maximizing k submodular functions and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved randomized algorithm for k submodular function maximization")
  Member "improved approximation algorithms for k submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved randomized algorithm for k submodular function maximization")
  Member "towards minimizing k submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond boolean surjective vcsps")
  Member "potts model parametric maxflow and k submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient optimization for hierarchically structured interacting segments hints")
  Member "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "adaptive submodular optimization under matroid constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive influence maximization in social networks why commit when you can adapt")

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.DS(Data Structures and Algorithms)