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 class of parallel tiled linear algebra algorithms for multicore architectures"
  Abstract: As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a major requirement and introduces the necessity of loose synchronization in the parallel execution of an operation. This paper presents an algorithm for the Cholesky, LU and QR factorization where the operations can be represented as a sequence of small tasks that operate on square blocks of data. These tasks can be dynamically scheduled for execution based on the dependencies among them and on the availability of computational resources. This may result in an out of order execution of the tasks which will completely hide the presence of intrinsically sequential tasks in the factorization. Performance comparisons are presented with the LAPACK algorithms where parallelism can only be exploited at the level of the BLAS operations and vendor implementations.

Connected through hyperedge HE-1 (induced by source paper "task based gpu accelerated and robust library for solving dense nonsymmetric eigenvalue problems"):
  with members:
    - "introduction to starneig a task based library for solving nonsymmetric eigenvalue problems"

Connected through hyperedge HE-2 (induced by source paper "the blas api of blasfeo optimizing performance for small matrices"):
  with members:
    - "embedded online optimization for model predictive control at megahertz rates"

Connected through hyperedge HE-3 (induced by source paper "programming parallel dense matrix factorizations with look ahead and openmp"):
  with members:
    - "two sided reduction to compact band forms with look ahead"
    - "a case for malleable thread level linear algebra libraries the lu factorization with partial pivoting"
    - "architecture aware configuration and scheduling of matrix multiplication on asymmetric multicore processors"

Connected through hyperedge HE-4 (induced by source paper "look ahead in the two sided reduction to compact band forms for symmetric eigenvalue problems and the svd"):
  with members:
    - "a case for malleable thread level linear algebra libraries the lu factorization with partial pivoting"
    - "high performance solvers for dense hermitian eigenproblems"

Connected through hyperedge HE-5 (induced by source paper "task parallel incomplete cholesky factorization using 2d partitioned block layout"):
  with members:
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-6 (induced by source paper "multi threaded dense linear algebra libraries for low power asymmetric multicore processors"):
  with members:
    - "revisiting conventional task schedulers to exploit asymmetry in arm big little architectures for dense linear algebra"
    - "architecture aware configuration and scheduling of matrix multiplication on asymmetric multicore processors"

Connected through hyperedge HE-7 (induced by source paper "an efficient multi core implementation of a novel hss structured multifrontal solver using randomized sampling"):
  with members:
    - "taking advantage of hybrid systems for sparse direct solvers via task based runtimes"

Connected through hyperedge HE-8 (induced by source paper "parallel computation of echelon forms"):
  with members:
    - "simultaneous computation of the row and column rank profiles"
    - "rank profile revealing gaussian elimination and the cup matrix decomposition"

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.MS(Mathematical Software)