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 "an optimized recurrent unit for ultra low power keyword spotting"):
  Abstract: There is growing interest in being able to run neural networks on sensors, wearables and internet-of-things (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resource-constrained edge devices. #R##N#To meet this need, our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection (AED). The proposed architecture is based on the gated recurrent unit (`GRU') but features optimizations that make it implementable on ultra-low power micro-controllers such as the Arm Cortex M0+. #R##N#Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is demonstrated to be highly efficient and suitable for short-duration AED and keyword spotting tasks. A single eGRU cell is 60x faster and 10x smaller than a GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks of varying complexities. #R##N#The practicality of eGRU is investigated in a wearable acoustic event detection application. An eGRU model is implemented and tested on the Arm Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the eGRU model compares favorably with a full precision GRU that is running on a workstation. The embedded eGRU model achieves a classification accuracy 95.3%, which is only 2% less than the full precision GRU.

Includes members:
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation")
  Member "cmsis nn efficient neural network kernels for arm cortex m cpus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "improving speech recognition by revising gated recurrent units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "distributed deep neural networks over the cloud the edge and end devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "run time deep model multiplexing")
  Member "deep neural networks are robust to weight binarization and other non linear distortions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "minimal gated unit for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "recurrent neural networks with limited numerical precision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recurrent neural networks an embedded computing perspective")

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.LG(Machine Learning)