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: "randomized contractions meet lean decompositions"
  Abstract: The randomized contractions technique, introduced by Chitnis et al. in 2012, is a robust framework for designing parameterized algorithms for graph separation problems. On high level, an algorithm in this framework recurses on balanced separators while possible, and in the leaves of the recursion uses high connectivity of the graph at hand to highlight a solution by color coding. #R##N#In 2014, a subset of the current authors showed that, given a graph $G$ and a budget $k$ for the cut size in the studied separation problem, one can compute a tree decomposition of $G$ with adhesions of size bounded in $k$ and with bags exhibiting the same high connectivity properties with respect to cuts of size at most $k$ as in the leaves of the recursion in the randomized contractions framework. This led to an FPT algorithm for the Minimum Bisection problem. #R##N#In this paper, we provide a new construction algorithm for a tree decomposition with the aforementioned properties, by using the notion of lean decompositions of Thomas. Our algorithm is not only arguably simpler than the one from 2014, but also gives better parameter bounds; in particular, we provide best possible high connectivity properties with respect to edge cuts. This allows us to provide $2^{O(k \log k)} n^{O(1)}$-time parameterized algorithms for Minimum Bisection, Steiner Cut, and Steiner Multicut.

Connected through hyperedge HE-1 (induced by source paper "on quasipolynomial multicut mimicking networks and kernelization of multiway cut problems"):
  with members:
    - "lp branching algorithms based on biased graphs"
    - "vertex sparsifiers new results from old techniques"
    - "deterministic truncation of linear matroids"
    - "on vertex sparsifiers with steiner nodes"
    - "mimicking networks and succinct representations of terminal cuts"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

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)