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 "pushing the limits of rnn compression"):
  Abstract: Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP). KPs can compress RNN layers by 16-38x with minimal accuracy loss. We show that KP can beat the task accuracy achieved by other state-of-the-art compression techniques (pruning and low-rank matrix factorization) across 4 benchmarks spanning 3 different applications, while simultaneously improving inference run-time.

Includes members:
  Member "ternary hybrid neural tree networks for highly constrained iot applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems")
  Member "run time efficient rnn compression for inference on edge devices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressing language models using doped kronecker products")
  Member "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "measuring scheduling efficiency of rnns for nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressing language models using doped kronecker products")
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "learning compressed transforms with low displacement rank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low memory neural network training a technical report")
  Member "hello edge keyword spotting on microcontrollers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "factorization tricks for lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressing language models using doped kronecker products")

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)