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 "an exponential speedup in parallel running time for submodular maximization without loss in approximation"):
  Abstract: In this paper we study the adaptivity of submodular maximization. Adaptivity quantifies the number of sequential rounds that an algorithm makes when function evaluations can be executed in parallel. Adaptivity is a fundamental concept that is heavily studied across a variety of areas in computer science, largely due to the need for parallelizing computation. For the canonical problem of maximizing a monotone submodular function under a cardinality constraint, it is well known that a simple greedy algorithm achieves a $1-1/e$ approximation and that this approximation is optimal for polynomial-time algorithms. Somewhat surprisingly, despite extensive efforts on submodular optimization for large-scale datasets, until very recently there was no known algorithm that achieves a constant factor approximation for this problem whose adaptivity is sublinear in the size of the ground set $n$. #R##N#Recent work by Balkanski and Singer describes an algorithm that obtains an approximation arbitrarily close to $1/3$ in $\mathcal{O}(\log n)$ adaptive rounds and shows that no algorithm can obtain a constant factor approximation in $\tilde{o}(\log n)$ adaptive rounds. This approach achieves an exponential speedup in adaptivity (and parallel running time) at the expense of approximation quality. #R##N#In this paper we describe a novel approach that yields an algorithm whose approximation is arbitrarily close to the optimal $1-1/e$ guarantee in $\mathcal{O}(\log n)$ adaptive rounds. This algorithm therefore achieves an exponential speedup in parallel running time for submodular maximization at the expense of an arbitrarily small loss in approximation quality. This guarantee is optimal in both approximation and adaptivity, up to lower order terms.

Includes members:
  Member "an adaptivity hierarchy theorem for property testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noisy source location on a line")
  Member "settling the query complexity of non adaptive junta testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "almost optimal testers for concise representations")
  Member "parallel algorithms for select and partition with noisy comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "the power of randomization distributed submodular maximization on massive datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularized submodular maximization at scale")
  Member "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "welfare maximization with limited interaction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "locally adaptive optimization adaptive seeding for monotone submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient approximation algorithms for adaptive seed minimization")
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")

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)