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 "compressing rnns for iot devices by 15 38x using kronecker products"):
  Abstract: Recurrent Neural Networks (RNN) can be large and compute-intensive, making them hard to deploy on resource constrained devices. As a result, there is a need for compression technique that can significantly compress recurrent neural networks, without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker products. We call the RNNs compressed using Kronecker products as Kronecker product Recurrent Neural Networks (KPRNNs). KPRNNs can compress the LSTM[22], GRU [9] and parameter optimized FastRNN [30] layers by 15 - 38x with minor loss in accuracy and can act as in-place replacement of most RNN cells in existing applications. By quantizing the Kronecker compressed networks to 8 bits, we further push the compression factor to 50x. We compare the accuracy and runtime of KPRNNs with other state-of-the-art compression techniques across 5 benchmarks spanning 3 different applications, showing its generality. Additionally, we show how to control the compression factors achieved by Kronecker products using a novel hybrid decomposition technique. We call the RNN cells compressed using Kronecker products with this control mechanism as hybrid Kronecker product RNNs (HKPRNN). Using HKPRNN, we compress RNN Cells in 2 benchmarks by 10x and 20x achieving better accuracy than other state-of-the-art compression techniques.

Includes members:
  Member "tucker tensor layer in fully connected neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning")
  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 "the state of sparsity in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "run time efficient rnn compression for inference on edge devices")
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "stabilizing gradients for deep neural networks via efficient svd parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalization and representational limits of graph neural networks")

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)