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: "heuristic algorithms for the maximum colorful subtree problem"
  Abstract: In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this resulted in the computational Maximum Colorful Subtree problem, which is NP-hard. Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem; and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured. #R##N#Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem against an exact algorithm. We put particular emphasis on whether a heuristic is able to rank candidates such that the correct solution is ranked highly. We propose this "intermediate" evaluation because evaluating the approximating quality of heuristics is misleading: Even a slightly suboptimal solution can be structurally very different from the true solution. On the other hand, we cannot structurally evaluate against the ground truth, as this is unknown. We find that particularly one of the heuristics consistently ranks the correct solution in a favorable position. Integrating the heuristic into the analysis pipeline results in a speedup of 10-fold or more, without sacrificing accuracy.

Connected through hyperedge HE-1 (induced by source paper "the maximum colorful arborescence problem parameterized by the structure of its color hierarchy graph"):
  with members:
    - "kernelization lower bounds by cross composition"

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)