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 "recurrent neural networks an embedded computing perspective"):
  Abstract: Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, a strong interest has emerged to execute RNNs on embedded devices. However, RNN requirements of high computational capability and large memory space is difficult to be met. In this paper, we review the existing implementations of RNN models on embedded platforms and discuss the methods adopted to overcome the limitations of embedded systems. #R##N#We define the objectives of mapping RNN algorithms on embedded platforms and the challenges facing their realization. Then, we explain the components of RNNs models from an implementation perspective. Furthermore, we discuss the optimizations applied on RNNs to run efficiently on embedded platforms. Additionally, we compare the defined objectives with the implementations and highlight some open research questions and aspects currently not addressed for embedded RNNs. #R##N#Overall, applying algorithmic optimizations on RNN models and decreasing the memory access overhead is vital to reach high efficiency. To further increase the achievable efficiency, the article points up the more promising optimizations to be applied in future research. Additionally, this article observes that high performance has been targeted by many implementations while flexibility was still less attempted. Thus, the article provides some guidelines for RNNs hardware designers to support flexibility in a better manner.

Includes members:
  Member "deep neural network approximation for custom hardware where we ve been where we re going":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "taurus an intelligent data plane")
  Member "netadapt platform aware neural network adaptation for mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "single stream parallelization of recurrent neural networks for low power and fast inference":
    also connected through other hyperedges:
      (none)
  Member "effective quantization approaches for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptivfloat a floating point based data type for resilient deep learning inference")
  Member "alternating multi bit quantization for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantized neural network inference with precision batching")
  Member "a survey on methods and theories of quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware")
  Member "tensor decomposition for compressing recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")

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.NE(Neural and Evolutionary Computing)