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 "streaming submodular maximization under a k set system constraint"):
  Abstract: In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization. This reduction readily leads to the currently tightest deterministic approximation ratio for submodular maximization subject to a $k$-matchoid constraint. Moreover, we propose the first streaming algorithm for monotone submodular maximization subject to $k$-extendible and $k$-set system constraints. Together with our proposed reduction, we obtain $O(k\log k)$ and $O(k^2\log k)$ approximation ratio for submodular maximization subject to the above constraints, respectively. We extensively evaluate the empirical performance of our algorithm against the existing work in a series of experiments including finding the maximum independent set in randomly generated graphs, maximizing linear functions over social networks, movie recommendation, Yelp location summarization, and Twitter data summarization.

Includes members:
  Member "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "an optimal streaming algorithm for non monotone submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized submodular maximization at scale")
  Member "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularized submodular maximization at scale")
  Member "data summarization at scale a two stage submodular approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularized submodular maximization at scale")
  Member "do less get more streaming submodular maximization with subsampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularized submodular maximization at scale")
  Member "beyond 1 2 approximation for submodular maximization on massive data streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized submodular maximization at scale")
  Member "discrete adversarial attacks and submodular optimization with applications to text classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "g odel s sentence is an adversarial example but unsolvable")
  Member "streaming non monotone submodular maximization personalized video summarization on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")

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)