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 "submodular optimization under noise"):
  Abstract: We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: #R##N#- For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; #R##N#- For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; #R##N#-If the noise is adversarial, no non-trivial approximation guarantee can be obtained.

Includes members:
  Member "when are welfare guarantees robust":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate modularity revisited")
  Member "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "tight bounds on low degree spectral concentration of submodular and xos functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "budget feasible mechanisms for experimental design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal feature discovery through strategic modification")
  Member "optimal bounds on approximation of submodular and xos functions by juntas":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "representation approximation and learning of submodular functions using low rank decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "learning coverage functions and private release of marginals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "the computational complexity of truthfulness in combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")

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)