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: "minimizing and computing the inverse geodesic length on trees"
  Abstract: The inverse geodesic length (IGL) of a graph $G=(V,E)$ is the sum of inverse distances between every two vertices: $IGL(G) = \sum_{\{u,v\} \subseteq V} \frac{1}{d_G(u,v)}$. In the MinIGL problem, the input is a graph $G$, an integer $k$, and a target inverse geodesic length $T$, and the question is whether there are $k$ vertices whose deletion decreases the IGL of $G$ to at most $T$. Aziz et al. (2018) proved that MinIGL is $W[1]$-hard for parameter treewidth, but the complexity status of the problem remains open in the case where $G$ is a tree. We show that MinIGL on trees is subexponential by giving a $2^{O\left((n \log n)^{5/6}\right)}$ time, polynomial space algorithm, where $n$ is the number of vertices of the input graph. #R##N#The distance distribution of a graph $G$ is a sequence $\{a_i\}$ describing the number of vertex pairs distance $i$ apart in $G$: $a_i = |\{ \{u, v\}: d_G(u, v) = i\}|$. Given only the distance distribution, one can easily determine graph parameters such as diameter, Wiener index, and particularly, the IGL. We show that the distance distribution of a tree can be computed in $O(n \log^2 n)$ time by reduction to polynomial multiplication. We also extend the result to graphs of bounded treewidth by showing that the first $P$ values of the distance distribution can be computed in $2^{O(\text{tw}(G))} n^{1 + \varepsilon} \sqrt{P}$ time.

Connected through hyperedge HE-1 (induced by source paper "computing the inverse geodesic length in planar graphs and graphs of bounded treewidth"):
  with members:
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"
    - "subquadratic algorithms for the diameter and the sum of pairwise distances in planar graphs"
    - "batched point location in sinr diagrams via algebraic tools"
    - "structured recursive separator decompositions for planar graphs in linear time"
    - "computing the distance between piecewise linear bivariate functions"

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)