human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "synergy an hw sw framework for high throughput cnns on embedded heterogeneous soc"):
  Abstract: Convolutional Neural Networks (CNN) have been widely deployed in diverse application domains. There has been significant progress in accelerating both their training and inference using high-performance GPUs, FPGAs, and custom ASICs for datacenter-scale environments. The recent proliferation of mobile and Internet of Things (IoT) devices have necessitated real-time, energy-efficient deep neural network inference on embedded-class, resource-constrained platforms. In this context, we present Synergy, an automated, hardware-software co-designed, pipelined, high-throughput CNN inference framework on embedded heterogeneous system-on-chip (SoC) architectures (Xilinx Zynq). Synergy leverages, through multi-threading, all the available on-chip resources, which includes the dual-core ARM processor along with the FPGA and the NEON Single-Instruction Multiple-Data (SIMD) engines as accelerators. Moreover, Synergy provides a unified abstraction of the heterogeneous accelerators (FPGA and NEON) and can adapt to different network configurations at runtime without changing the underlying hardware accelerator architecture by balancing workload across accelerators through work-stealing. Synergy achieves 7.3X speedup, averaged across seven CNN models, over a well-optimized software-only solution. Synergy demonstrates substantially better throughput and energy-efficiency compared to the contemporary CNN implementations on the same SoC architecture.

Includes members:
  Member "fpga based cnn inference accelerator synthesized from multi threaded c software":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tma tera macs w neural hardware inference accelerator with a multiplier less massive parallel processor")
  Member "finn a framework for fast scalable binarized neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "maximizing cnn accelerator efficiency through resource partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an energy efficient fpga based deconvolutional neural networks accelerator for single image super resolution")
  Member "understanding the impact of precision quantization on the accuracy and energy of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "alert accurate anytime learning for energy and timeliness")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

Question: Please tell me which class the source paper 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)