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: "2 node connectivity network design"
  Abstract: We consider network design problems in which we are given a graph $G=(V,E)$ with edge costs, and seek a min-cost/size $2$-node-connected subgraph $G'=(V',E')$ that satisfies a prescribed property. In the Block-Tree Augmentation problem the goal is to augment a tree $T$ by a min-size edge set $F \subseteq E$ such that $G'=T \cup F$ is $2$-node-connected. We break the natural ratio of $2$ for this problem and show that it admits approximation ratio $1.91$. This result extends to the related Crossing Family Augmentation problem. In the $2$-Connected Dominating Set problem $G'$ should dominate $V$. We give the first non-trivial approximation algorithm for this problem, with expected ratio $\tilde{O}(\log^4 |V|)$. In the $2$-Connected Quota Subgraph problem we are given node profits $p(v)$ and $G'$ should have profit at least a given quota $Q$. We show expected ratio $\tilde{O}(\log^2|V|)$, almost matching the best known ratio $O(\log^2|V|)$. #R##N#Our algorithms are very simple, and they combine three main ingredients. 1. A probabilistic spanning tree embedding with distortion $\tilde{O}(\log |V|)$ results in a variant of the Block-Tree Augmentation problem. 2. An approximation ratio preserving reduction of Block-Tree Augmentation variants to Node Weighted Steiner Tree problems. 3. Using existing approximation algorithms for variants of the Node Weighted Steiner Tree problem.

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)