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: "petfmm a dynamically load balancing parallel fast multipole library"
  Abstract: Fast algorithms for the computation of N-body problems can be broadly classified into mesh-based interpolation methods, and hierarchical or multiresolution methods. To this latter class belongs the well-known fast multipole method (FMM), which offers (N) complexity. The FMM is a complex algorithm, and the programming difficulty associated with it has arguably diminished its impact, being a barrier for adoption. This paper presents an extensible parallel library for N-body interactions utilizing the FMM algorithm. A prominent feature of this library is that it is designed to be extensible, with a view to unifying efforts involving many algorithms based on the same principles as the FMM and enabling easy development of scientific application codes. The paper also details an exhaustive model for the computation of tree-based N-body algorithms in parallel, including both work estimates and communications estimates. With this model, we are able to implement a method to provide automatic, a priori load balancing of the parallel execution, achieving optimal distribution of the computational work among processors and minimal inter-processor communications. Using a client application that performs the calculation of velocity induced by N vortex particles in two dimensions, ample verification and testing of the library was performed. Strong scaling results are presented with 10 million particles on up to 256 processors, including both speedup and parallel efficiency. The largest problem size that has been run with the PetFMM library at this point was 64 million particles in 64 processors. The library is currently able to achieve over 85% parallel efficiency for 64 processes. The performance study, computational model, and application demonstrations presented in this paper are limited to 2D. However, the software architecture was designed to make an extension of this work to 3D straightforward, as the framework is templated over the dimension. The software library is open source under the PETSc license, even less restrictive than the BSD license; this guarantees the maximum impact to the scientific community and encourages peer-based collaboration for the extensions and applications. Copyright © 2010 John Wiley & Sons, Ltd.

Connected through hyperedge HE-1 (induced by source paper "extreme scale fmm accelerated boundary integral equation solver for wave scattering"):
  with members:
    - "communication reducing algorithms for distributed hierarchical n body problems with boundary distributions"
    - "fast multipole preconditioners for sparse matrices arising from elliptic equations"
    - "an fmm based on dual tree traversal for many core architectures"

Connected through hyperedge HE-2 (induced by source paper "adaptive fast multipole methods on the gpu"):
  with members:
    - "a tuned and scalable fast multipole method as a preeminent algorithm for exascale systems"

Connected through hyperedge HE-3 (induced by source paper "a tuned and scalable fast multipole method as a preeminent algorithm for exascale systems"):
  with members:
    - "petascale turbulence simulation using a highly parallel fast multipole method on gpus"
    - "biomolecular electrostatics using a fast multipole bem on up to 512 gpus and a billion unknowns"

Connected through hyperedge HE-4 (induced by source paper "data driven execution of fast multipole methods"):
  with members:
    - "petascale turbulence simulation using a highly parallel fast multipole method on gpus"

Connected through hyperedge HE-5 (induced by source paper "pipelining the fast multipole method over a runtime system"):
  with members:
    - "data driven execution of fast multipole methods"

Connected through hyperedge HE-6 (induced by source paper "optimal scalable forward models for computing gravity anomalies"):
  with members:
    - "petrbf a parallel o n algorithm for radial basis function interpolation with gaussians"
    - "mesh algorithms for pde with sieve i mesh distribution"

Connected through hyperedge HE-7 (induced by source paper "how to obtain efficient gpu kernels an illustration using fmm fgt algorithms"):
  with members:
    - "characterization of the accuracy of the fast multipole method in particle simulations"

Connected through hyperedge HE-8 (induced by source paper "removing the barrier to scalability in parallel fmm"):
  with members:
    - "characterization of the accuracy of the fast multipole method in particle simulations"

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.DC(Distributed, Parallel, and Cluster Computing)