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: "maximum betweenness centrality approximability and tractable cases"
  Abstract: The Maximum Betweenness Centrality problem (MBC) can be defined as follows. Given a graph find a $k$-element node set $C$ that maximizes the probability of detecting communication between a pair of nodes $s$ and $t$ chosen uniformly at random. It is assumed that the communication between $s$ and $t$ is realized along a shortest $s$--$t$ path which is, again, selected uniformly at random. The communication is detected if the communication path contains a node of $C$. Recently, Dolev et al. (2009) showed that MBC is NP-hard and gave a $(1-1/e)$-approximation using a greedy approach. We provide a reduction of MBC to Maximum Coverage that simplifies the analysis of the algorithm of Dolev et al. considerably. Our reduction allows us to obtain a new algorithm with the same approximation ratio for a (generalized) budgeted version of MBC. We provide tight examples showing that the analyses of both algorithms are best possible. Moreover, we prove that MBC is APX-complete and provide an exact polynomial-time algorithm for MBC on tree graphs.

Connected through hyperedge HE-1 (induced by source paper "current flow group closeness centrality for complex networks"):
  with members:
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "scaling up group closeness maximization"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "scalable betweenness centrality maximization via sampling"
    - "measuring and maximizing influence via random walk in social activity networks"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "absorbing random walk centrality theory and algorithms"
    - "axioms for centrality"

Connected through hyperedge HE-2 (induced by source paper "scalable betweenness centrality maximization via sampling"):
  with members:
    - "tri est counting local and global triangles in fully dynamic streams with fixed memory size"
    - "tree decompositions and social graphs"
    - "a divide and conquer algorithm for betweenness centrality"
    - "triadic measures on graphs the power of wedge sampling"
    - "axioms for centrality"
    - "maximizing social influence in nearly optimal time"
    - "shattering and compressing networks for centrality analysis"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

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)