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: "expression templates revisited a performance analysis of current methodologies"
  Abstract: In the last decade, expression templates (ETs) have gained a reputation as an efficient performance optimization tool for C++ codes. This reputation builds on several ET-based linear algebra frameworks focused on combining both elegant and high-performance C++ code. However, on closer examination the assumption that ETs are a performance optimization technique cannot be maintained. In this paper we compare the performance of several generations of ET-based frameworks. We analyze different ET methodologies and explain the inability of some ET implementations to deliver high performance for dense and sparse linear algebra operations. Additionally, we introduce the notion of “smart” ETs, which truly allow for a combination of high performance code with the elegance and maintainability of a domain-specific language.

Connected through hyperedge HE-1 (induced by source paper "learning to play the chess variant crazyhouse above world champion level with deep neural networks and human data"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "eraserelu a simple way to ease the training of deep convolution neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rethink relu to training better cnns"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "super convergence very fast training of residual networks using large learning rates"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "practical sparse matrices in c with hybrid storage and template based expression optimisation"):
  with members:
    - "a user friendly hybrid sparse matrix class in c"
    - "ensmallen a flexible c library for efficient function optimization"
    - "c templates as partial evaluation"

Connected through hyperedge HE-6 (induced by source paper "scheduling optimization of parallel linear algebra algorithms using supervised learning"):
  with members:
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "hpx smart executors"

Connected through hyperedge HE-7 (induced by source paper "eigen ad algorithmic differentiation of the eigen library"):
  with members:
    - "high performance derivative computations using codipack"
    - "the stan math library reverse mode automatic differentiation in c"

Connected through hyperedge HE-8 (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"

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.PF(Performance)