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: "submodular maximization over sliding windows"
  Abstract: In this paper we study the extraction of representative elements in the data stream model in the form of submodular maximization. Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. We provide a general reduction from the sliding window model to the standard streaming model, and thus our approach works for general constraints as long as there is a corresponding streaming algorithm in the standard streaming model. As a consequence, we obtain the first algorithms in the sliding window model for maximizing a monotone/non-monotone submodular function under cardinality and matroid constraints. We also propose several heuristics and show their efficiency in real-world datasets.

Connected through hyperedge HE-1 (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"
    - "better streaming algorithms for the maximum coverage problem"

Connected through hyperedge HE-2 (induced by source paper "numerical linear algebra in the sliding window model"):
  with members:
    - "nearly optimal distinct elements and heavy hitters on sliding windows"
    - "lectures on randomized numerical linear algebra"
    - "submodular optimization over sliding windows"
    - "sketches for matrix norms faster smaller and more general"
    - "online row sampling"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "frequent directions simple and deterministic matrix sketching"
    - "sparsified cholesky solvers for sdd linear systems"

Connected through hyperedge HE-3 (induced by source paper "tracking influential nodes in time decaying dynamic interaction networks"):
  with members:
    - "real time influence maximization on dynamic social streams"
    - "tracking influential individuals in dynamic networks"
    - "submodular optimization over sliding windows"
    - "robust influence maximization"
    - "boosting information spread an algorithmic approach"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-4 (induced by source paper "nearly optimal distinct elements and heavy hitters on sliding windows"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "continuous monitoring of ell_p norms in data streams"
    - "submodular optimization over sliding windows"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "heavy hitters via cluster preserving clustering"
    - "beating countsketch for heavy hitters in insertion streams"
    - "how to catch l 2 heavy hitters on sliding windows"
    - "sketching and streaming entropy via approximation theory"

Connected through hyperedge HE-5 (induced by source paper "submodular optimization over streams with inhomogeneous decays"):
  with members:
    - "data summarization at scale a two stage submodular approach"
    - "submodular optimization over sliding windows"
    - "lazier than lazy greedy"

Connected through hyperedge HE-6 (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"
    - "better streaming algorithms for the maximum coverage problem"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "submodular optimization over sliding windows"):
  with members:
    - "the role of social networks in information diffusion"
    - "continuous monitoring of distributed data streams over a time based sliding window"

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)