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 "current flow group closeness centrality for complex networks"):
  Abstract: The problem of selecting a group of vertices under certain constraints that maximize their joint centrality arises in many practical scenarios. In this paper, we extend the notion of current flow closeness centrality (CFCC) to a set of vertices in a graph, and investigate the problem of selecting a subset S to maximizes its CFCC C(S), with the cardinality constraint |S| = k. We show the NP-hardness of the problem, but propose two greedy algorithms to minimize the reciprocal of C(S). We prove the approximation ratios by showing the monotonicity and supermodularity. A proposed deterministic greedy algorithm has an approximation factor and cubic running time. To compare with, a proposed randomized algorithm gives -approximation in nearly-linear time, for any ? > 0. Extensive experiments on model and real networks demonstrate the effectiveness and efficiency of the proposed algorithms, with the randomized algorithm being applied to massive networks with more than a million vertices.

Includes members:
  Member "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "scaling up group closeness maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "maximizing influence based group shapley centrality")
  Member "estimating current flow closeness centrality with a multigrid laplacian solver":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation")
  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 "measuring and maximizing influence via random walk in social activity networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a benchmark to evaluate infiniband solutions for java applications")
  Member "a faster cutting plane method and its implications for combinatorial and convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "absorbing random walk centrality theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimizing node discovery on networks problem definitions fast algorithms and observations")
  Member "axioms for centrality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "maximizing influence based group shapley centrality")

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)