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: "fully dynamic approximation of betweenness centrality"
  Abstract: Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in evolving networks. In previous work we proposed the first semi-dynamic algorithms that recompute an approximation of betweenness in connected graphs after batches of edge insertions. #R##N#In this paper we propose the first fully-dynamic approximation algorithms (for weighted and unweighted undirected graphs that need not to be connected) with a provable guarantee on the maximum approximation error. The transfer to fully-dynamic and disconnected graphs implies additional algorithmic problems that could be of independent interest. In particular, we propose a new upper bound on the vertex diameter for weighted undirected graphs. For both weighted and unweighted graphs, we also propose the first fully-dynamic algorithms that keep track of such upper bound. In addition, we extend our former algorithm for semi-dynamic BFS to batches of both edge insertions and deletions. #R##N#Using approximation, our algorithms are the first to make in-memory computation of betweenness in fully-dynamic networks with millions of edges feasible. Our experiments show that they can achieve substantial speedups compared to recomputation, up to several orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "simple and fast distributed computation of betweenness centrality"):
  with members:
    - "distributed estimation and control of node centrality in undirected asymmetric networks"
    - "distributed algorithms for directed betweenness centrality and all pairs shortest paths"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"

Connected through hyperedge HE-2 (induced by source paper "global rank estimation"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "literature survey on interplay of topics information diffusion and connections on social networks"
    - "group centrality for semantic networks a swot analysis featuring random walks"
    - "centrality measures in networks based on nodes attributes long range interactions and group influence"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"

Connected through hyperedge HE-3 (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"
    - "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"
    - "decremental all pairs all shortest paths and betweenness centrality"

Connected through hyperedge HE-4 (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"
    - "networkit an interactive tool suite for high performance network analysis"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-5 (induced by source paper "approximating betweenness centrality in fully dynamic networks"):
  with members:
    - "fast and simple computation of top k closeness centralities"
    - "a faster algorithm for fully dynamic betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"
    - "betweenness centrality incremental and faster"

Connected through hyperedge HE-6 (induced by source paper "a faster algorithm for fully dynamic betweenness centrality"):
  with members:
    - "scalable online betweenness centrality in evolving graphs"
    - "decremental all pairs all shortest paths and betweenness centrality"
    - "fully dynamic all pairs all shortest paths"
    - "betweenness centrality incremental and faster"

Connected through hyperedge HE-7 (induced by source paper "networkit a tool suite for large scale complex network analysis"):
  with members:
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "approximating betweenness centrality in large evolving networks"
    - "social structure of facebook networks"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

Connected through hyperedge HE-8 (induced by source paper "an efficient heuristic for betweenness ordering"):
  with members:
    - "approximating betweenness centrality in large evolving networks"
    - "a divide and conquer algorithm for betweenness centrality"
    - "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)