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: "fault tolerant virtual backbone in heterogeneous wireless sensor network"
  Abstract: To save energy and alleviate interference, connected dominating set (CDS) was proposed to serve as a virtual backbone of wireless sensor networks (WSNs). Because sensor nodes may fail due to accidental damages or energy depletion, it is desirable to construct a fault tolerant virtual backbone with high redundancy in both coverage and connectivity. This can be modeled as a    $k$   -connected    $m$   -fold dominating set (abbreviated as    $(k,m)$   -CDS) problem. A node set    $C\subseteq V(G)$    is a    $(k,m)$   -CDS of graph    $G$    if every node in    $V(G)\backslash C$    is adjacent with at least    $m$    nodes in    $C$    and the subgraph of    $G$    induced by    $C$    is    $k$   -connected. Constant approximation algorithm is known for    $(3,m)$   -CDS in unit disk graph, which models homogeneous WSNs. In this paper, we present the first performance guaranteed approximation algorithm for    $(3,m)$   -CDS in a  heterogeneous  WSN. In fact, our performance ratio is valid for any topology. The performance ratio is at most    $\gamma $   , where    $\gamma =\alpha +8+2\ln (2\alpha -6)$    for    $\alpha \geq 4$    and    $\gamma =3\alpha +2\ln 2$    for    $\alpha   , and    $\alpha $    is the performance ratio for the minimum    $(2,m)$   -CDS problem. Using currently best known value of    $\alpha $   , the performance ratio is    $\ln \delta +o(\ln \delta )$   , where    $\delta $    is the maximum degree of the graph, which is asymptotically best possible in view of the non-approximability of the problem. Applying our algorithm on a unit disk graph, the performance ratio is less than 27, improving previous ratio 62.3 by a large amount for the    $(3,m)$   -CDS problem on a unit disk graph.

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.DM(Discrete Mathematics)