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 "tensor decomposition for compressing recurrent neural network"):
  Abstract: In the machine learning fields, Recurrent Neural Network (RNN) has become a popular algorithm for sequential data modeling. However, behind the impressive performance, RNNs require a large number of parameters for both training and inference. In this paper, we are trying to reduce the number of parameters and maintain the expressive power from RNN simultaneously. We utilize several tensor decompositions method including CANDECOMP/PARAFAC (CP), Tucker decomposition and Tensor Train(TT) to re-parameterize the Gated Recurrent Unit (GRU) RNN. We evaluate all tensor-based RNNs performance on sequence modeling tasks with a various number of parameters. Based on our experiment results, TT-GRU achieved the best results in a various number of parameters compared to other decomposition methods.

Includes members:
  Member "tensor train recurrent neural networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "compressing recurrent neural network with tensor train":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "learning compact recurrent neural networks with block term tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gate decorator global filter pruning method for accelerating deep convolutional neural networks")
  Member "sequence level knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel machine translation with disentangled context transformer")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "a simple way to initialize recurrent networks of rectified linear units":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "deep learning with limited numerical precision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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)