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: "efficient streaming algorithms for submodular maximization with multi knapsack constraints"
  Abstract: Submodular maximization (SM) has become a silver bullet for a broad class of applications such as influence maximization, data summarization, top-$k$ representative queries, and recommendations. In this paper, we study the SM problem in data streams. Most existing algorithms for streaming SM only support the append-only model with cardinality constraints, which cannot meet the requirements of real-world problems considering either the data recency issues or more general $d$-knapsack constraints. Therefore, we first propose an append-only streaming algorithm {\sc KnapStream} for SM subject to a $d$-knapsack constraint (SMDK). Furthermore, we devise the {\sc KnapWindow} algorithm for SMDK over sliding windows to capture the recency constraints. Theoretically, the proposed algorithms have constant approximation ratios for a fixed number of knapsacks and sublinear complexities. We finally evaluate the efficiency and effectiveness of our algorithms in two real-world datasets. The results show that the proposed algorithms achieve two orders of magnitude speedups over the greedy baseline in the batch setting while preserving high quality solutions.

Connected through hyperedge HE-1 (induced by source paper "do less get more streaming submodular maximization with subsampling"):
  with members:
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"
    - "distributed submodular maximization"
    - "submodular maximization meets streaming matchings matroids and more"

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)