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: "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 $\kappa$.

Connected through hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "towards nearly linear time algorithms for submodular maximization with a matroid constraint"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"

Connected through hyperedge HE-2 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "the fast algorithm for submodular maximization"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "unconstrained submodular maximization with constant adaptive complexity"

Connected through hyperedge HE-3 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "approximate k cover in hypergraphs efficient algorithms and applications"):
  with members:
    - "outward influence and cascade size estimation in billion scale networks"
    - "scalable betweenness centrality maximization via sampling"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "tight bounds on the round complexity of the distributed maximum coverage problem"):
  with members:
    - "distributed partial clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "on estimating maximum matching size in graph streams"
    - "combinatorial auctions do need modest interaction"
    - "submodular optimization over sliding windows"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "submodular maximization over sliding windows"

Connected through hyperedge HE-6 (induced by source paper "dynamic set cover improved algorithms lower bounds"):
  with members:
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "distributed pcp theorems for hardness of approximation in p"
    - "dynamic algorithms for graph coloring"
    - "decremental single source reachability in planar digraphs"

Connected through hyperedge HE-7 (induced by source paper "beyond 1 2 approximation for submodular maximization on massive data streams"):
  with members:
    - "deletion robust submodular maximization at scale"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "online submodular maximization with preemption"
    - "a new framework for distributed submodular maximization"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"

Connected through hyperedge HE-8 (induced by source paper "tight space approximation tradeoff for the multi pass streaming set cover problem"):
  with members:
    - "submodular optimization over sliding windows"
    - "almost optimal streaming algorithms for coverage problems"
    - "submodular maximization over sliding windows"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "semi streaming set cover"
    - "analytical approach to parallel repetition"

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)