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: "time and space optimal algorithms for the many visits tsp"
  Abstract: The many-visits traveling salesperson problem (MV-TSP) asks for an optimal tour of $n$ cities that visits each city $c$ a prescribed number $k_c$ of times. Travel costs may not be symmetric, and visiting a city twice in a row may incur a non-zero cost. The MV-TSP problem finds applications in scheduling, geometric approximation, and Hamiltonicity of certain graph families. #R##N#The fastest known algorithm for MV-TSP is due to Cosmadakis and Papadimitriou (SICOMP, 1984). It runs in time $n^{O(n)} + O(n^3 \log \sum_c k_c )$ and requires $n^{\Omega(n)}$ space. #R##N#The interesting feature of the Cosmadakis-Papadimitriou algorithm is its \emph{logarithmic} dependence on the total length $\sum_c k_c$ of the tour, allowing the algorithm to handle instances with very long tours, beyond what is tractable in the standard TSP setting. However, the \emph{superexponential} dependence on the number of cities in both its time and space complexity renders the algorithm impractical for all but the narrowest range of this parameter. #R##N#In this paper we significantly improve on the Cosmadakis-Papadimitriou algorithm, giving an MV-TSP algorithm that runs in \emph{single-exponential} time with \emph{polynomial} space. More precisely, we obtain the running time $2^{O(n)} + O(n^3 \log{\sum_c k_c})$, with $O(n^2 \log{\sum_c k_c})$ space. Assuming the Exponential-time Hypothesis (ETH), both the time and space requirements of our algorithm are optimal. #R##N#Our algorithm is deterministic, and arguably both simpler and easier to analyse than the original approach of Cosmadakis and Papadimitriou. It involves an optimization over oriented spanning trees of a graph and employs a recursive, centroid-based decomposition of trees.

Connected through hyperedge HE-1 (induced by source paper "degree bounded generalized polymatroids and approximating the metric many visits tsp"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "matroidal degree bounded minimum spanning trees"
    - "on generalizations of network design problems with degree bounds"

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)