human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "improving the betweenness centrality of a node by adding links"):
  Abstract: Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network. In several scenarios, having a high betweenness can have a positive impact on the node itself. Hence, in this paper we consider the problem of determining how much a vertex can increase its centrality by creating a limited amount of new edges incident to it. In particular, we study the problem of maximizing the betweenness score of a given node -- Maximum Betweenness Improvement (MBI) -- and that of maximizing the ranking of a given node -- Maximum Ranking Improvement (MRI). We show that MBI cannot be approximated in polynomial-time within a factor $(1-\frac{1}{2e})$ and that MRI does not admit any polynomial-time constant factor approximation algorithm, both unless $P=NP$. We then propose a simple greedy approximation algorithm for MBI with an almost tight approximation ratio and we test its performance on several real-world networks. We experimentally show that our algorithm highly increases both the betweenness score and the ranking of a given node ant that it outperforms several competitive baselines. To speed up the computation of our greedy algorithm, we also propose a new dynamic algorithm for updating the betweenness of one node after an edge insertion, which might be of independent interest. Using the dynamic algorithm, we are now able to compute an approximation of MBI on networks with up to $10^5$ edges in most cases in a matter of seconds or a few minutes.

Includes members:
  Member "computing top k closeness centrality faster in unweighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximizing influence based group shapley centrality")
  Member "faster betweenness centrality updates in evolving networks":
    also connected through other hyperedges:
      (none)
  Member "kadabra is an adaptive algorithm for betweenness via random approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")
  Member "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simple and fast distributed computation of betweenness centrality")
  Member "scalable betweenness centrality maximization via sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach")
  Member "approximating betweenness centrality in fully dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach")
  Member "approximation algorithms for reducing the spectral radius to control epidemic spread":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transient dynamics of epidemic spreading and its mitigation on large networks")
  Member "networkit a tool suite for large scale complex network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")

Question: Please tell me which class the source paper 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)