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: "accelerating scientific computations with mixed precision algorithms"
  Abstract: Abstract   On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. The approach presented here can apply not only to conventional processors but also to other technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the STI Cell BE processor. Results on modern processor architectures and the STI Cell BE are presented.    Program summary   Program title:  ITER-REF   Catalogue identifier:  AECO_v1_0   Program summary URL:  http://cpc.cs.qub.ac.uk/summaries/AECO_v1_0.html    Program obtainable from:  CPC Program Library, Queen's University, Belfast, N. Ireland   Licensing provisions:  Standard CPC licence,  http://cpc.cs.qub.ac.uk/licence/licence.html    No. of lines in distributed program, including test data, etc.:  7211   No. of bytes in distributed program, including test data, etc.:  41 862   Distribution format:  tar.gz   Programming language:  FORTRAN 77   Computer:  desktop, server   Operating system:  Unix/Linux   RAM:  512 Mbytes   Classification:  4.8   External routines:  BLAS (optional)   Nature of problem:  On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution.   Solution method:  Mixed precision algorithms stem from the observation that, in many cases, a single precision solution of a problem can be refined to the point where double precision accuracy is achieved. A common approach to the solution of linear systems, either dense or sparse, is to perform the LU factorization of the coefficient matrix using Gaussian elimination. First, the coefficient matrix  A  is factored into the product of a lower triangular matrix  L  and an upper triangular matrix  U . Partial row pivoting is in general used to improve numerical stability resulting in a factorization    P  A  =  L  U   , where  P  is a permutation matrix. The solution for the system is achieved by first solving    L  y  =  P  b    (forward substitution) and then solving    U  x  =  y    (backward substitution). Due to round-off errors, the computed solution,  x , carries a numerical error magnified by the condition number of the coefficient matrix  A . In order to improve the computed solution, an iterative process can be applied, which produces a correction to the computed solution at each iteration, which then yields the method that is commonly known as the iterative refinement algorithm. Provided that the system is not too ill-conditioned, the algorithm produces a solution correct to the working precision.   Running time:  seconds/minutes

Connected through hyperedge HE-1 (induced by source paper "a note on solving nonlinear optimization problems in variable precision"):
  with members:
    - "training deep neural networks with 8 bit floating point numbers"
    - "doing moore with less leapfrogging moore s law with inexactness for supercomputing"

Connected through hyperedge HE-2 (induced by source paper "deep learning with limited numerical precision"):
  with members:
    - "deep image scaling up image recognition"
    - "learning machines implemented on non deterministic hardware"
    - "training deep neural networks with low precision multiplications"
    - "low precision arithmetic for deep learning"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-3 (induced by source paper "numerical integration on gpus for higher order finite elements"):
  with members:
    - "finite element integration on gpus"
    - "vectorized opencl implementation of numerical integration for higher order finite elements"
    - "dolfin automated finite element computing"

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)