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: "distance constrained labeling on graphs with bounded neighborhood diversity"
  Abstract: We study the complexity of a group of distance-constrained graph labeling problems when parameterized by the neighborhood diversity ($nd$), which is a natural graph parameter between vertex cover and clique width. Neighborhood diversity has been used to generalize and speed up \sf{FPT} algorithms previously parameterized by vertex cover, as is also demonstrated by our paper. #R##N#We show that the {\sc Uniform Channel Assignment} problem is fixed parameter tractable when parameterized by $nd$ and the largest weight and that every {\sc $L(p_1, p_2,\dots, p_k)$-labeling} problem is \sf{FPT} when parameterized by $nd$, maximum $p_i$ and $k$. #R##N#These results furthermore yield an \sf{FPT} algorithms for {\sc $L(p_1, p_2,\dots, p_k)$-labeling} and {\sc Channel Assignment} problems when parameterized by vertex cover size, answering an open question of Fiala et al.: {\emph Parameterized complexity of coloring problems: Treewidth versus vertex cover}, generalizing their results beyond $L(2,1)$-labeling, and tightening the complexity gap to clique width, where the problems are already hard for constant clique width.

Connected through hyperedge HE-1 (induced by source paper "evangelism in social networks algorithms and complexity"):
  with members:
    - "target set selection in dense graph classes"
    - "on finding small sets that influence large networks"
    - "spread of influence in weighted networks under time and budget constraints"
    - "contagious sets in dense graphs"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "using neighborhood diversity to solve hard problems"
    - "algorithmic meta theorems"

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)