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: "a divide and conquer algorithm for betweenness centrality"
  Abstract: The problem of efficiently computing the betweenness centrality of nodes has been researched extensively. To date, the best known exact and centralized algorithm for this task is an algorithm proposed in 2001 by Brandes. The contribution of our paper is Brandes++, an algorithm for exact efficient computation of betweenness centrality. The crux of our algorithm is that we create a sketch of the graph, that we call the skeleton, by replacing subgraphs with simpler graph structures. Depending on the underlying graph structure, using this skeleton and by keeping appropriate summaries Brandes++ we can achieve significantly low running times in our computations. Extensive experimental evaluation on real life datasets demonstrate the efficacy of our algorithm for different types of graphs. We release our code for benefit of the research community.

Connected through hyperedge HE-1 (induced by source paper "an adaptive version of brandes algorithm for betweenness centrality"):
  with members:
    - "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
    - "polynomial fixed parameter algorithms a case study for longest path on interval graphs"
    - "betweenness centrality incremental and faster"

Connected through hyperedge HE-2 (induced by source paper "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"):
  with members:
    - "refining approximating betweenness centrality based on samplings"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "approximating betweenness centrality in fully dynamic networks"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"

Connected through hyperedge HE-3 (induced by source paper "kadabra is an adaptive algorithm for betweenness via random approximation"):
  with members:
    - "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "metropolis hastings algorithms for estimating betweenness centrality in large networks"
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "bubing massive crawling for the masses"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"

Connected through hyperedge HE-4 (induced by source paper "scalable betweenness centrality maximization via sampling"):
  with members:
    - "tri est counting local and global triangles in fully dynamic streams with fixed memory size"
    - "tree decompositions and social graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "axioms for centrality"
    - "maximizing social influence in nearly optimal time"
    - "shattering and compressing networks for centrality analysis"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-5 (induced by source paper "an efficient heuristic for betweenness ordering"):
  with members:
    - "fully dynamic approximation of betweenness centrality"
    - "approximating betweenness centrality in large evolving networks"
    - "betweenness centrality incremental and faster"
    - "betweenness centrality algorithms and lower bounds"
    - "fast approximation of centrality"

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)