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: "towards improving brandes algorithm for betweenness centrality"
  Abstract: Betweenness centrality, measuring how many shortest paths pass through a vertex, is one of the most important network analysis concepts for assessing the (relative) importance of a vertex. The famous state-of-art algorithm of Brandes [2001] computes the betweenness centrality of all vertices in O(mn) worst-case time on an n-vertex and m-edge graph. In practical follow-up work, significant empirical speedups were achieved by preprocessing degree-one vertices. We extend this by showing how to also deal with degree-two vertices (turning out to be much richer in mathematical structure than the case of degree-one vertices). For our new betweenness centrality algorithm we prove the running time upper bound O(kn), where k is the size of a minimum feedback edge set of the input graph.

Connected through hyperedge HE-1 (induced by source paper "parameterized complexity of diameter"):
  with members:
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "data reduction for maximum matching on real world graphs theory and experiments"
    - "multivariate analysis of orthogonal range searching and graph distances parameterized by treewidth"
    - "efficient and adaptive parameterized algorithms on modular decompositions"
    - "parameterized aspects of triangle enumeration"
    - "fully polynomial fpt algorithms for some classes of bounded clique width graphs"
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"
    - "when can graph hyperbolicity be computed in linear time"

Connected through hyperedge HE-2 (induced by source paper "on the structure of unique shortest paths in graphs"):
  with members:
    - "linear size distance preservers"
    - "near linear query complexity for graph inference"
    - "faster all pairs shortest paths via circuit complexity"
    - "graph reconstruction via distance oracles"

Connected through hyperedge HE-3 (induced by source paper "when can graph hyperbolicity be computed in linear time"):
  with members:
    - "fully polynomial fpt algorithms for some classes of bounded clique width graphs"
    - "polynomial fixed parameter algorithms a case study for longest path on interval graphs"
    - "computing the gromov hyperbolicity of a discrete metric space"

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)