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: "strategy preserving compilation for parallel functional code"
  Abstract: Graphics Processing Units (GPUs) and other parallel devices are widely available and have the potential for accelerating a wide class of algorithms. However, expert programming skills are required to achieving maximum performance. hese devices expose low-level hardware details through imperative programming interfaces where programmers explicity encode device-specific optimisation strategies. This inevitably results in non-performance-portable programs delivering suboptimal performance on other devices. #R##N#Functional programming models have recently seen a renaissance in the systems community as they offer possible solutions for tackling the performance portability challenge. Recent work has shown how to automatically choose high-performance parallelisation strategies for a wide range of hardware architectures encoded in a functional representation. However, the translation of such functional representations to the imperative program expected by the hardware interface is typically performed ad hoc with no correctness guarantees and no guarantees to preserve the intended parallelisation strategy. #R##N#In this paper, we present a formalised strategy-preserving translation from high-level functional code to low-level data race free parallel imperative code. This translation is formulated and proved correct within a language we call Data Parallel Idealised Algol (DPIA), a dialect of Reynolds' Idealised Algol. Performance results on GPUs and a multicore CPU show that the formalised translation process generates low-level code with performance on a par with code generated from ad hoc approaches.

Connected through hyperedge HE-1 (induced by source paper "a language for describing optimization strategies"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "porgy strategy driven interactive transformation of graphs"

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.DC(Distributed, Parallel, and Cluster Computing)