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: "fast approximations for metric tsp via linear programming"
  Abstract: We develop faster approximation algorithms for Metric-TSP building on recent, nearly linear time approximation schemes for the LP relaxation [Chekuri and Quanrud, 2017]. We show that the LP solution can be sparsified via cut-sparsification techniques such as those of Benczur and Karger [2015]. Given a weighted graph $G$ with $m$ edges and $n$ vertices, and $\epsilon > 0$, our randomized algorithm outputs with high probability a $(1+\epsilon)$-approximate solution to the LP relaxation whose support has $\operatorname{O}(n \log n /\epsilon^2)$ edges. The running time of the algorithm is $\operatorname{\~O}(m/\epsilon^2)$. This can be generically used to speed up algorithms that rely on the LP. #R##N#For Metric-TSP, we obtain the following concrete result. For a weighted graph $G$ with $m$ edges and $n$ vertices, and $\epsilon > 0$, we describe an algorithm that outputs with high probability a tour of $G$ with cost at most $(1 + \epsilon) \frac{3}{2}$ times the minimum cost tour of $G$ in time $\operatorname{\~O}(m/\epsilon^2 + n^{1.5}/\epsilon^3)$. Previous implementations of Christofides' algorithm [Christofides, 1976] require, for a $\frac{3}{2}$-optimal tour, $\operatorname{\~O}(n^{2.5})$ time when the metric is explicitly given, or $\operatorname{\~O}(\min\{m^{1.5}, mn+n^{2.5}\})$ time when the metric is given implicitly as the shortest path metric of a weighted graph.

Connected through hyperedge HE-1 (induced by source paper "fast and deterministic approximations for k cut"):
  with members:
    - "lp relaxation and tree packing for minimum k cuts"
    - "on approximating sparse covering integer programs"
    - "faster exact and approximate algorithms for k cut"
    - "an fpt algorithm beating 2 approximation for k cut"
    - "approximating the held karp bound for metric tsp in nearly linear time"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"
    - "designing fpt algorithms for cut problems using randomized contractions"

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)