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 the approximation of submodular functions"):
  Abstract: Submodular functions are a fundamental object of study in combinatorial optimization, economics, machine learning, etc. and exhibit a rich combinatorial structure. Many subclasses of submodular functions have also been well studied and these subclasses widely vary in their complexity. Our motivation is to understand the relative complexity of these classes of functions. Towards this, we consider the question of how well can one class of submodular functions be approximated by another (simpler) class of submodular functions. Such approximations naturally allow algorithms designed for the simpler class to be applied to the bigger class of functions. We prove both upper and lower bounds on such approximations. #R##N#Our main results are: #R##N#1. General submodular functions can be approximated by cut functions of directed graphs to a factor of $n^2/4$, which is tight. #R##N#2. General symmetric submodular functions$^{1}$ can be approximated by cut functions of undirected graphs to a factor of $n-1$, which is tight up to a constant. #R##N#3. Budgeted additive functions can be approximated by coverage functions to a factor of $e/(e-1)$, which is tight. #R##N#Here $n$ is the size of the ground set on which the submodular function is defined. #R##N#We also observe that prior works imply that monotone submodular functions can be approximated by coverage functions with a factor between $O(\sqrt{n} \log n)$ and $\Omega(n^{1/3} /\log^2 n) $.

Includes members:
  Member "a truthful randomized mechanism for combinatorial public projects via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic verification for obviously strategyproof mechanisms")
  Member "secondary spectrum auctions for symmetric and submodular bidders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "is submodularity testable":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "submodular approximation sampling based algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal approximation for unconstrained non submodular minimization")

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)