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 "efficient representative subset selection over sliding windows"):
  Abstract: Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. Existing literature models RSS as the submodular maximization problem to capture the “diminishing returns” property of the representativeness of selected subsets, but often only has a single constraint (e.g., cardinality), which limits its applications in many real-world problems. To capture the data recency issue and support different types of constraints, we formulate dynamic RSS in data streams as maximizing submodular functions subject to general $d$d-knapsack constraints (SMDK) over sliding windows. We propose a KnapWindow framework (KW) for SMDK. KW utilizes the KnapStream algorithm (KS) for SMDK in append-only streams as a subroutine. It maintains a sequence of checkpoints and KS instances over the sliding window. Theoretically, KW is $\frac{1-\varepsilon }{1+d}$1-ɛ1+d-approximate for SMDK. Furthermore, we propose a KnapWindowPlus framework (KW$^{+}$+) to improve upon KW. KW$^{+}$+ builds an index SubKnapChk to manage the checkpoints and KS instances. SubKnapChk deletes a checkpoint whenever it can be approximated by its successors. By keeping much fewer checkpoints, KW$^{+}$+ achieves higher efficiency than KW while still guaranteeing a $\frac{1-\varepsilon ^{\prime }}{2+2d}$1-ɛ'2+2d-approximate solution for SMDK. Finally, we evaluate the efficiency and solution quality of KW and KW$^{+}$+ in real-world datasets. The experimental results demonstrate that KW achieves more than two orders of magnitude speedups over the batch baseline and preserves high-quality solutions for SMDK over sliding windows. KW$^{+}$+ further runs 5-10 times faster than KW while providing solutions with equivalent or even better utilities.

Includes members:
  Member "real time influence maximization on dynamic social streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locec local community based edge classification in large online social networks")
  Member "submodular optimization over sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bring your own greedy max near optimal 1 2 approximations for submodular knapsack")
  Member "adaptive maximization of pointwise submodular functions with budget constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian active learning with abstention feedbacks")

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)