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: "a unified approach to source location via rooted network augmentation"
  Abstract: In Source Location (SL) problems the goal is to select a minimum cost source set $S$ such that the connectivity from $S$ to any node $v$ is at least the demand $d_v$ of $v$. In Network Augmentation (NA) problems we are given a graph $G=(V,E)$ and an edge-set $F$ on $V$, edge-costs on $F$ or node-costs on $V$, and connectivity requirements ${r_{sv}:sv \in D}$ on a set $D$ of "demand edges". The goal is to compute a minimum cost edge-set $I \subseteq F$, such that in the graph $G+I$, the connectivity of each $sv \in D$ is at least $r_{sv}$. In Rooted NA $D$ is a star with center $s$, and in $a$-Based NA $F$ is a star with center $a$. We suggest a notion of $q$-connectivity, where every node $v$ has a capacity $q_v \geq 1$ and show that a large class of SL problems, including the variants that appear in the literature, is a particular case of $q$-connectivity $s$-Based Rooted NA problems. We use this to derive some approximation algorithms for SL from those for NA, as well as to derive some new results for SL problems. Some of our results are as follows. (i) We show that the $(1 + \ln |D|)$-approximation algorithm of Kortsarz and nutov for $a$-Based NA with edge-costs has ratio $1 + \ln \min{\sum_{uv \in D} r_{uv},|D| \cdot p_{\max}}$ for the $q$-connectivity version with node-costs, where $p_{\max}$ is the maximum number of parallel edges in $F$. In particular, this implies by a simple proof the $(1 + \ln \sum_{v \in V} d_v)$-approximation algorithms of Sakashita, Makino, and Fujishige for three SL variants. Moreover, we will show that this is so whenever the connectivity function is submodular. (ii) For undirected $a$-Based NA we give an approximation algorithm with ratio $\min{k,p_{\max} \ln k} \cdot O(\ln k)$ for node-costs and $O(\ln^2 k)$ for edge-costs; the latter improves the ratio $\tilde{O}(k)$ of Fukunaga.

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)