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: "coarse grain performance estimator for heterogeneous parallel computing architectures like zynq all programmable soc"
  Abstract: Heterogeneous computing is emerging as a mandatory requirement for power-efficient system design. With this aim, modern heterogeneous platforms like Zynq All-Programmable SoC, that integrates ARM-based SMP and programmable logic, have been designed. However, those platforms introduce large design cycles consisting on hardware/software partitioning, decisions on granularity and number of hardware accelerators, hardware/software integration, bitstream generation, etc. #R##N#This paper presents a performance parallel heterogeneous estimation for systems where hardware/software co-design and run-time heterogeneous task scheduling are key. The results show that the programmer can quickly decide, based only on her/his OmpSs (OpenMP + extensions) application, which is the co-design that achieves nearly optimal heterogeneous parallel performance, based on the methodology presented and considering only synthesis estimation results. The methodology presented reduces the programmer co-design decision from hours to minutes and shows high potential on hardware/software heterogeneous parallel performance estimation on the Zynq All-Programmable SoC.

Connected through hyperedge HE-1 (induced by source paper "pyramid machine learning framework to estimate the optimal timing and resource usage of a high level synthesis design"):
  with members:
    - "machine learning based routing congestion prediction in fpga high level synthesis"
    - "benchmarking the capabilities and limitations of sat solvers in defeating obfuscation schemes"
    - "tensorflow a system for large scale machine learning"

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)