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: "performance prediction of finite difference solvers for different computer architectures"
  Abstract: Abstract   The life-cycle of a partial differential equation (PDE) solver is often characterized by three development phases: the development of a stable numerical discretization; development of a correct (verified) implementation; and the optimization of the implementation for different computer architectures. Often it is only after significant time and effort has been invested that the performance bottlenecks of a PDE solver are fully understood, and the precise details varies between different computer architectures. One way to mitigate this issue is to establish a reliable performance model that allows a numerical analyst to make reliable predictions of how well a numerical method would perform on a given computer architecture, before embarking upon potentially long and expensive implementation and optimization phases. The availability of a reliable performance model also saves developer effort as it both informs the developer on what kind of optimisations are beneficial, and when the maximum expected performance has been reached and optimisation work should stop. We show how discretization of a wave-equation can be theoretically studied to understand the performance limitations of the method on modern computer architectures. We focus on the roofline model, now broadly used in the high-performance computing community, which considers the achievable performance in terms of the peak memory bandwidth and peak floating point performance of a computer with respect to algorithmic choices. A first principles analysis of operational intensity for key time-stepping finite-difference algorithms is presented. With this information available at the time of algorithm design, the expected performance on target computer systems can be used as a driver for algorithm design.

Connected through hyperedge HE-1 (induced by source paper "devito v3 1 0 an embedded domain specific language for finite differences and geophysical exploration"):
  with members:
    - "simflowny 2 an upgraded platform for scientific modelling and simulation"
    - "architecture and performance of devito a system for automated stencil computation"
    - "high level python abstractions for optimal checkpointing in inversion problems"
    - "optimised finite difference computation from symbolic equations"
    - "firedrake automating the finite element method by composing abstractions"
    - "opensbli a framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "automated derivation of the adjoint of high level transient finite element programs"

Connected through hyperedge HE-2 (induced by source paper "performance of devito on hpc optimised arm processors"):
  with members:
    - "architecture and performance of devito a system for automated stencil computation"

Connected through hyperedge HE-3 (induced by source paper "devito an embedded domain specific language for finite differences and geophysical exploration"):
  with members:
    - "simflowny 2 an upgraded platform for scientific modelling and simulation"
    - "architecture and performance of devito a system for automated stencil computation"
    - "high level python abstractions for optimal checkpointing in inversion problems"
    - "opensbli a framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "automated derivation of the adjoint of high level transient finite element programs"

Connected through hyperedge HE-4 (induced by source paper "architecture and performance of devito a system for automated stencil computation"):
  with members:
    - "devito v3 1 0 an embedded domain specific language for finite differences and geophysical exploration"
    - "simflowny 2 an upgraded platform for scientific modelling and simulation"
    - "opensbli a framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures"
    - "a metric for performance portability"
    - "loo py transformation based code generation for gpus and cpus"

Connected through hyperedge HE-5 (induced by source paper "optimised finite difference computation from symbolic equations"):
  with members:
    - "automated derivation of the adjoint of high level transient finite element programs"

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)