human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an optimal streaming algorithm for non monotone submodular maximization"
  Abstract: We study the problem of maximizing a non-monotone submodular function subject to a size constraint in the streaming model. Our main contribution is a single-pass streaming algorithm that uses $k\cdot\mathrm{poly}(1/\epsilon)$ memory, where $k$ is the size constraint. At the end of the stream, we post-process the output of the algorithm using any offline algorithm for submodular maximization, and we obtain a solution whose approximation guarantee is $\frac{\alpha}{1+\alpha}-\epsilon$, where $\alpha$ is the approximation of the offline algorithm. If we use an exact (exponential time) post-processing algorithm, we obtain a $\frac{1}{2}-\epsilon$ approximation, almost matching the lower bound of [Alaluf-Feldman, 2019]. If we post-process with the algorithm of [Buchbinder-Feldman, Math of OR 2019] that achieves the currently best approximation guarantee $\alpha=0.385$, we obtain a $0.2779$ approximation in polynomial time, improving over the previously best polynomial-time approximation of $0.2335$ due to [Alaluf-Feldman, 2019]. In addition to its improved approximation guarantee, our algorithm enjoys a fast update time and overall running time.

Connected through hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale"):
  with members:
    - "flexible modeling of diversity with strongly log concave distributions"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "strong log concavity does not imply log submodularity"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "data summarization at scale a two stage submodular approach"
    - "a tight approximation for submodular maximization with mixed packing and covering constraints"
    - "do less get more streaming submodular maximization with subsampling"

Connected through hyperedge HE-2 (induced by source paper "streaming submodular maximization under a k set system constraint"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "data summarization at scale a two stage submodular approach"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "greed is good near optimal submodular maximization via greedy optimization"

Question: Please tell me which class the center node 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)