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 "a new framework for distributed submodular maximization"):
  Abstract: A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.

Includes members:
  Member "the power of randomization distributed submodular maximization on massive datasets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "randomized composable core sets for distributed submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized submodular maximization at scale")
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "comparing apples and oranges query tradeoff in submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interlaced greedy algorithm for maximization of submodular functions in nearly linear time")
  Member "parallel algorithms for geometric graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "communication steps for parallel query processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "sorting searching and simulation in the mapreduce framework":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "symmetry and approximability of submodular maximization problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")

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)