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: "characterization of the accuracy of the fast multipole method in particle simulations"
  Abstract: The fast multipole method (FMM) is a fast summation algorithm capable of accelerating pairwise interaction calculations, known as N-body problems, from an algorithmic complexity of (N2) to (N) for N particles. The algorithm has brought a dramatic increase in the capability of particle simulations in many application areas, such as electrostatics, particle formulations of fluid mechanics, and others. Although the literature on the subject provides theoretical error bounds for the FMM approximation, there are not many reports on the measured errors in a suite of computational experiments that characterize the accuracy of the method in relation with the different parameters available to the user. We have performed such an experimental investigation, and summarized the results of about 1500 calculations using the FMM algorithm, applied to the 2D vortex particle method. In addition to the more standard diagnostic of the maximum error, we supply illustrations of the spatial distribution of the errors, offering visual evidence of all the contributing factors to the overall approximation accuracy: multipole expansion, local expansion, hierarchical spatial decomposition (interaction lists, local domain, far domain). This presentation is a contribution to any researcher wishing to incorporate the FMM acceleration to their application code, as it aids in understanding where accuracy is gained or compromised. Copyright © 2009 John Wiley & Sons, Ltd.

Connected through hyperedge HE-1 (induced by source paper "how to obtain efficient gpu kernels an illustration using fmm fgt algorithms"):
  with members:
    - "petfmm a dynamically load balancing parallel fast multipole library"

Connected through hyperedge HE-2 (induced by source paper "petfmm a dynamically load balancing parallel fast multipole library"):
  with members:
    - "mesh algorithms for pde with sieve i mesh distribution"

Connected through hyperedge HE-3 (induced by source paper "removing the barrier to scalability in parallel fmm"):
  with members:
    - "petfmm a dynamically load balancing parallel fast multipole library"

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)