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 "learning compact recurrent neural networks with block term tensor decomposition"):
  Abstract: Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.

Includes members:
  Member "tensor regression networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "tensor train recurrent neural networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "tensorial recurrent neural networks for longitudinal data analysis":
    also connected through other hyperedges:
      (none)
  Member "sharing residual units through collective tensor factorization in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional neural networks with transformed input based on robust tensor network decomposition")
  Member "long term forecasting using higher order tensor rnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "learning efficient tensor representations with ring structure networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable model for tensor ring completion")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")

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)