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 fmm based on dual tree traversal for many core architectures"
  Abstract: The present work attempts to integrate the independent efforts in the fast N-body community to create the fastest N-body library for many-core and heterogenous architectures. Focus is placed on low accuracy optimizations, in response to the recent interest to use FMM as a preconditioner for sparse linear solvers. A direct comparison with other state-of-the-art fast N-body codes demonstrates that orders of magnitude increase in performance can be achieved by careful selection of the optimal algorithm and low-level optimization of the code. The current N-body solver uses a fast multipole method with an efficient strategy for finding the list of cell-cell interactions by a dual tree traversal. A task-based threading model is used to maximize thread-level parallelism and intra-node load-balancing. In order to extract the full potential of the SIMD units on the latest CPUs, the inner kernels are optimized using AVX instructions. Our code -- exaFMM -- is an order of magnitude faster than the current state-of-the-art FMM codes, which are themselves an order of magnitude faster than the average FMM code.

Connected through hyperedge HE-1 (induced by source paper "effect of mixed precision computing on h matrix vector multiplication in bem analysis"):
  with members:
    - "mixed precision training"

Connected through hyperedge HE-2 (induced by source paper "fft fmm and multigrid on the road to exascale performance challenges and opportunities"):
  with members:
    - "communication reducing algorithms for distributed hierarchical n body problems with boundary distributions"
    - "fast multipole method as a matrix free hierarchical low rank approximation"
    - "fast multipole preconditioners for sparse matrices arising from elliptic equations"

Connected through hyperedge HE-3 (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"
    - "petfmm a dynamically load balancing parallel fast multipole library"

Connected through hyperedge HE-4 (induced by source paper "learning with analytical models"):
  with members:
    - "optimization of an electromagnetics code with multicore wavefront diamond blocking and multi dimensional intra tile parallelization"
    - "communication complexity of the fast multipole method and its algebraic variants"
    - "fast multipole preconditioners for sparse matrices arising from elliptic equations"

Connected through hyperedge HE-5 (induced by source paper "dynamic autotuning of adaptive fast multipole methods on hybrid multicore cpu and gpu systems"):
  with members:
    - "adaptive fast multipole methods on the gpu"

Connected through hyperedge HE-6 (induced by source paper "fast multipole preconditioners for sparse matrices arising from elliptic equations"):
  with members:
    - "a fast block low rank dense solver with applications to finite element matrices"
    - "petiga high performance isogeometric analysis"

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.NA(Numerical Analysis)