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: "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
  Abstract: Betweenness Centrality (BC) is a widely used metric of the relevance of a node in a network. The fastest-known algorithm for the evaluation of BC on unweighted graphs builds a tree representing information about the shortest paths for each vertex to calculate its contribution to the BC score. Actually, for specific vertices, the shortest-path trees of neighboring nodes could be leveraged to reduce the computational burden, but existing BC algorithms do not exploit that information and carry out redundant computations. We propose a new algorithm, called dynamic merging of frontiers, which makes use of such information to derive the BC score of degree-2 vertices by re-using the results of the sub-trees of the neighbors. We implemented our idea in parallel fashion exploiting Graphics Processing Units. Compared to state-of-the-art implementations, our approach achieves a linear improvement in the number of degree-2 vertices and an average improvement of × over a variety of real-world graphs.

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

Connected through hyperedge HE-2 (induced by source paper "kadabra is an adaptive algorithm for betweenness via random approximation"):
  with members:
    - "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"
    - "into the square on the complexity of quadratic time solvable problems"

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.DC(Distributed, Parallel, and Cluster Computing)