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 "maximizing submodular functions with the diminishing return property over the integer lattice"):
  Abstract: The problem of maximizing non-negative monotone submodular functions under a certain constraint has been intensively studied in the last decade. In this paper, we address the problem for functions defined over the integer lattice. Suppose that a non-negative monotone submodular function $f:\mathbb{Z}_+^n \to \mathbb{R}_+$ is given via an evaluation oracle. Furthermore, we assume that $f$ satisfies the diminishing return property, which is not an immediate consequence of the submodularity when the domain is the integer lattice. Then, we show (i) a $(1-1/e-\epsilon)$-approximation algorithm for a cardinality constraint with $\widetilde{O}(\frac{n}{\epsilon}\log \frac{r}{\epsilon})$ queries, where $r$ is the maximum cardinality of feasible solutions, (ii) a $(1-1/e-\epsilon)$-approximation algorithm for a polymatroid constraint with $\widetilde{O}(\frac{nr}{\epsilon^4}+n^6)$ queries, where $r$ is the rank of the polymatroid, and (iii) a $(1-1/e-\epsilon)$-approximation algorithm for a knapsack constraint with $\widetilde{O}(\frac{n^2}{\epsilon^{18}}\log \frac{1}{w})(\frac{1}{\epsilon})^{O(1/\epsilon^8)}$ queries, where $w$ is the minumum weight of elements. #R##N#Our algorithms for polymatroid constraints and knapsack constraints first extend the domain of the objective function to the Euclidean space and then run the continuous greedy algorithm. We give two different kinds of continuous extensions, one is for knapsack constraints and the other is for polymatroid constraints, which might be of independent interest.

Includes members:
  Member "how to influence people with partial incentives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable lattice influence maximization")
  Member "online submodular welfare maximization greedy is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")

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)