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 optimal approximation for submodular maximization under a matroid constraint in the adaptive complexity model"):
  Abstract: In this paper we study submodular maximization under a matroid constraint in the adaptive complexity model. This model was recently introduced in the context of submodular optimization in [BS18a] to quantify the information theoretic complexity of black-box optimization in a parallel computation model. Informally, the adaptivity of an algorithm is the number of sequential rounds it makes when each round can execute polynomially-many function evaluations in parallel. Since submodular optimization is regularly applied on large datasets we seek algorithms with low adaptivity to enable speedups via parallelization. Consequently, a recent line of work has been devoted to designing constant factor approximation algorithms for maximizing submodular functions under various constraints in the adaptive complexity model [BS18a, BS18b, BBS18, BRS19, EN19, FMZ19, CQ19, ENV18, FMZ18]. #R##N#Despite the burst in work on submodular maximization in the adaptive complexity model the fundamental problem of maximizing a monotone submodular function under a matroid constraint has remained elusive. In particular, all known techniques fail for this problem and there are no known constant factor approximation algorithms whose adaptivity is sublinear in the rank of the matroid $k$ or in the worst case sublinear in the size of the ground set $n$. #R##N#In this paper we present an approximation algorithm for the problem of maximizing a monotone submodular function under a matroid constraint in the adaptive complexity model. The approximation guarantee of the algorithm is arbitrarily close to the optimal $1-1/e$ and it has near optimal adaptivity of $O(\log(n)\log(k))$. This result is obtained using a novel technique of adaptive sequencing which departs from previous techniques for submodular maximization in the adaptive complexity model.

Includes members:
  Member "an exponential speedup in parallel running time for submodular maximization without loss in approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "non monotone submodular maximization with nearly optimal adaptivity complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "submodular maximization with matroid and packing constraints in parallel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast parallel algorithms for feature selection")
  Member "submodular function maximization in parallel via the multilinear relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "submodular maximization with optimal approximation adaptivity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "non monotone submodular maximization in exponentially fewer iterations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "dependent randomized rounding for matroid polytopes and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint")

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)