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: "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
  Abstract: This paper discusses an efficient parallel implementation of the ensemble Kalman filter based on the modified Cholesky decomposition. The proposed implementation starts with decomposing the domain into sub-domains. In each sub-domain a sparse estimation of the inverse background error covariance matrix is computed via a modified Cholesky decomposition; the estimates are computed concurrently on separate processors. The sparsity of this estimator is dictated by the conditional independence of model components for some radius of influence. Then, the assimilation step is carried out in parallel without the need of inter-processor communication. Once the local analysis states are computed, the analysis sub-domains are mapped back onto the global domain to obtain the analysis ensemble. Computational experiments are performed using the Atmospheric General Circulation Model (SPEEDY) with the T-63 resolution on the Blueridge cluster at Virginia Tech. The number of processors used in the experiments ranges from 96 to 2,048. The proposed implementation outperforms in terms of accuracy the well-known local ensemble transform Kalman filter (LETKF) for all the model variables. The computational time of the proposed implementation is similar to that of the parallel LETKF method (where no covariance estimation is performed). Finally, for the largest number of processors, the proposed parallel implementation is 400 times faster than the serial version of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "linnea automatic generation of efficient linear algebra programs"):
  with members:
    - "the linear algebra mapping problem"
    - "automatic generation of efficient linear algebra programs"
    - "the generalized matrix chain algorithm"
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "efficient pattern matching in python"
    - "matchpy a pattern matching library"
    - "sparsity based correction of exponential artifacts"
    - "on a natural dynamics for linear programming"

Connected through hyperedge HE-2 (induced by source paper "automatic generation of efficient linear algebra programs"):
  with members:
    - "the generalized matrix chain algorithm"
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "efficient pattern matching in python"
    - "image restoration by iterative denoising and backward projections"
    - "matchpy a pattern matching library"
    - "sparsity based correction of exponential artifacts"
    - "on a natural dynamics for linear programming"
    - "a study on the influence of caching sequences of dense linear algebra kernels"

Connected through hyperedge HE-3 (induced by source paper "the generalized matrix chain algorithm"):
  with members:
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "deriving correct high performance algorithms"
    - "a dual method for computing power transfer distribution factors"
    - "sparsity based correction of exponential artifacts"
    - "consistent discretization and minimization of the l1 norm on manifolds"
    - "the elaps framework experimental linear algebra performance studies"
    - "on a natural dynamics for linear programming"
    - "a study on the influence of caching sequences of dense linear algebra kernels"

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)