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: "an experimental evaluation of the best of many christofides algorithm for the traveling salesman problem"
  Abstract: Recent papers on approximation algorithms for the traveling salesman problem (TSP) have given a new variant on the well-known Christofides' algorithm for the TSP, called the Best-of-Many Christofides' algorithm. The algorithm involves sampling a spanning tree from the solution the standard LP relaxation of the TSP, subject to the condition that each edge is sampled with probability at most its value in the LP relaxation. One then runs Christofides' algorithm on the tree by computing a minimum-cost matching on the odd-degree vertices in the tree, and shortcutting the resulting Eulerian graph to a tour. In this paper we perform an experimental evaluation of the Best-of-Many Christofides' algorithm to see if there are empirical reasons to believe its performance is better than that of Christofides' algorithm. Furthermore, several different sampling schemes have been proposed; we implement several different schemes to determine which ones might be the most promising for obtaining improved performance guarantees over that of Christofides' algorithm. In our experiments, all of the implemented methods perform significantly better than the Christofides' algorithm; an algorithm that samples from a maximum entropy distribution over spanning trees seems to be particularly good, though there are others that perform almost as well.

Connected through hyperedge HE-1 (induced by source paper "fast approximations for metric tsp via linear programming"):
  with members:
    - "approximating the held karp bound for metric tsp in nearly linear time"

Connected through hyperedge HE-2 (induced by source paper "approximating the held karp bound for metric tsp in nearly linear time"):
  with members:
    - "incremental exact min cut in poly logarithmic amortized update time"
    - "unified acceleration method for packing and covering problems via diameter reduction"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"
    - "improved inapproximability for tsp"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-3 (induced by source paper "reassembling trees for the traveling salesman"):
  with members:
    - "better s t tours by gao trees"
    - "on the metric s t path traveling salesman problem"
    - "eight fifth approximation for the path tsp"
    - "approximating minimum cost connected t joins"
    - "improving christofides algorithm for the s t path tsp"
    - "approximating graphic tsp by matchings"
    - "13 9 approximation for graphic tsp"

Connected through hyperedge HE-4 (induced by source paper "better s t tours by gao trees"):
  with members:
    - "layers and matroids for the traveling salesman s paths"
    - "reassembling trees for the traveling salesman"
    - "on the metric s t path traveling salesman problem"
    - "eight fifth approximation for the path tsp"
    - "approximating minimum cost connected t joins"
    - "improving christofides algorithm for the s t path tsp"

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)