human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "recombinator k means a population based algorithm that exploits k means for recombination"):
  Abstract: We present a simple heuristic algorithm for efficiently optimizing the notoriously hard "minimum sum-of-squares clustering" problem, usually addressed by the classical k-means heuristic and its variants. The algorithm, called recombinator-k-means, is very similar to a genetic algorithmic scheme: it uses populations of configurations, that are optimized independently in parallel and then recombined in a next-iteration population batch by exploiting a variant of the k-means++ seeding algorithm. An additional reweighting mechanism ensures that the population eventually coalesces into a single solution. Extensive tests measuring optimization objective vs computational time on synthetic and real-word data show that it is the only choice, among state-of-the-art alternatives (simple restarts, random swap, genetic algorithm with pairwise-nearest-neighbor crossover), that consistently produces good results at all time scales, outperforming competitors on large and complicated datasets. The only parameter that requires tuning is the population size. The scheme is rather general (it could be applied even to k-medians or k-medoids, for example). Our implementation is publicly available at https://github.com/carlobaldassi/RecombinatorKMeans.jl.

Includes members:
  Member "a comparative study of efficient initialization methods for the k means clustering algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "scalable k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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.LG(Machine Learning)