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 "sketching with test scores and submodular maximization"):
  Abstract: We consider the problem of maximizing a submodular set function that can be expressed as the expected value of a function of an $n$-size collection of independent random variables with given prior distributions. This is a combinatorial optimization problem that arises in many applications, including the team selection problem that arises in the context of online labour platforms. We consider a class of approximation algorithms that are restricted to use some statistics of the prior distributions to estimate the outputs of value oracle calls, which can be interpreted as test scores. We establish that the existence of test scores that provide an approximation guarantee is equivalent to that a special type of test scores, so called replication test scores, provide this approximation guarantee. We also identify sufficient conditions for replication test scores to provide this approximation guarantee. These sufficient conditions are shown to hold for a large number of models of production, including a monotone concave function of total production, best-shot, and constant elasticity of substitution production function. We also study a more general submodular welfare maximization problem, which accommodates the case of multiple projects, and derive an $\Omega(1/\log(k))$-approximation, algorithm where $k$ is the maximum number of participants in a project. We evaluate the quality of approximation provided by test scores in a case study using data from a popular online labour platform for software development.

Includes members:
  Member "submodular optimization under noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms")
  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 "team performance with test scores":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "submodular maximization using test scores")
  Member "faster and simpler sketches of valuation functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "submodular maximization using test scores")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "optimal bounds on approximation of submodular and xos functions by juntas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "optimal approximation for submodular and supermodular optimization with bounded curvature":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized submodular maximization at scale")
  Member "combinatorial auctions with decreasing marginal utilities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "complexity theory game theory and economics the barbados lectures")

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)