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 "better streaming algorithms for the maximum coverage problem"):
  Abstract: We study the classic NP-Hard problem of finding the maximum $k$-set coverage in the data stream model: given a set system of $m$ sets that are subsets of a universe $\{1,\ldots,n \}$, find the $k$ sets that cover the most number of distinct elements. The problem can be approximated up to a factor $1-1/e$ in polynomial time. In the streaming-set model, the sets and their elements are revealed online. The main goal of our work is to design algorithms, with approximation guarantees as close as possible to $1-1/e$, that use sublinear space $o(mn)$. Our main results are: #R##N#Two $(1-1/e-\epsilon)$ approximation algorithms: One uses $O(\epsilon^{-1})$ passes and $\tilde{O}(\epsilon^{-2} k)$ space whereas the other uses only a single pass but $\tilde{O}(\epsilon^{-2} m)$ space. #R##N#We show that any approximation factor better than $(1-(1-1/k)^k)$ in constant passes requires $\Omega(m)$ space for constant $k$ even if the algorithm is allowed unbounded processing time. We also demonstrate a single-pass, $(1-\epsilon)$ approximation algorithm using $\tilde{O}(\epsilon^{-2} m \cdot \min(k,\epsilon^{-1}))$ space. #R##N#We also study the maximum $k$-vertex coverage problem in the dynamic graph stream model. In this model, the stream consists of edge insertions and deletions of a graph on $N$ vertices. The goal is to find $k$ vertices that cover the most number of distinct edges. #R##N#We show that any constant approximation in constant passes requires $\Omega(N)$ space for constant $k$ whereas $\tilde{O}(\epsilon^{-2}N)$ space is sufficient for a $(1-\epsilon)$ approximation and arbitrary $k$ in a single pass. #R##N#For regular graphs, we show that $\tilde{O}(\epsilon^{-3}k)$ space is sufficient for a $(1-\epsilon)$ approximation in a single pass. We generalize this to a $(\kappa-\epsilon)$ approximation when the ratio between the minimum and maximum degree is bounded below by $\kappa$.

Includes members:
  Member "correlation clustering in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair correlation clustering")
  Member "tight space approximation tradeoff for the multi pass streaming set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "almost optimal streaming algorithms for coverage problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "tight bounds for single pass streaming complexity of the set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "towards tight bounds for the streaming set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "densest subgraph in dynamic graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "streaming algorithms for submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized submodular maximization at scale")
  Member "incidence geometries and the pass complexity of semi streaming set cover":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")

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)