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: "knowledge based automatic generation of partitioned matrix expressions"
  Abstract: In a series of papers it has been shown that for many linear algebra operations it is possible to generate families of algorithms by following a systematic procedure. Although powerful, such a methodology involves complex algebraic manipulation, symbolic computations and pattern matching, making the generation a process challenging to be performed by hand. We aim for a fully automated system that from the sole description of a target operation creates multiple algorithms without any human intervention. Our approach consists of three main stages. The first stage yields the core object for the entire process, the Partitioned Matrix Expression (PME), which establishes how the target problem may be decomposed in terms of simpler sub-problems. In the second stage the PME is inspected to identify predicates, the Loop-Invariants, to be used to set up the skeleton of a family of proofs of correctness. In the third and last stage the actual algorithms are constructed so that each of them satisfies its corresponding proof of correctness. In this paper we focus on the first stage of the process, the automatic generation of Partitioned Matrix Expressions. In particular, we discuss the steps leading to a PME and the knowledge necessary for a symbolic system to perform such steps. We also introduce Cl1ck, a prototype system written in Mathematica that generates PMEs automatically.

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"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"

Connected through hyperedge HE-2 (induced by source paper "the linear algebra mapping problem"):
  with members:
    - "automatic generation of efficient linear algebra programs"
    - "clblast a tuned opencl blas library"
    - "the generalized matrix chain algorithm"
    - "image restoration by iterative denoising and backward projections"
    - "recursive algorithms for dense linear algebra the relapack collection"
    - "sparsity based correction of exponential artifacts"
    - "on a natural dynamics for linear programming"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-3 (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"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
    - "on a natural dynamics for linear programming"

Connected through hyperedge HE-4 (induced by source paper "program generation for linear algebra using multiple layers of dsls"):
  with members:
    - "automatic generation of loop invariants for matrix operations"

Connected through hyperedge HE-5 (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"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "program generation for small scale linear algebra applications"):
  with members:
    - "julia a fresh approach to numerical computing"
    - "automatic generation of loop invariants for matrix operations"
    - "application tailored linear algebra algorithms a search based approach"

Connected through hyperedge HE-7 (induced by source paper "a simple methodology for computing families of algorithms"):
  with members:
    - "knowledge based automatic generation of linear algebra algorithms and code"
    - "automatic generation of loop invariants for matrix operations"

Connected through hyperedge HE-8 (induced by source paper "large scale linear regression development of high performance routines"):
  with members:
    - "solving sequences of generalized least squares problems on multi threaded architectures"
    - "computing petaflops over terabytes of data the case of genome wide association studies"

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)