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 "compact autoregressive network"):
  Abstract: Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, the huge amount of parameters in the network lead to expensive computational cost and low learning efficiency. The problem can be alleviated slightly by introducing one more narrow hidden layer to the network, but the sample size required to achieve a certain training error is still large. To address this challenge, we rearrange the weight matrices of a linear autoregressive network into a tensor form, and then make use of Tucker decomposition to represent low-rank structures. This leads to a novel compact autoregressive network, called Tucker AutoRegressive (TAR) net. Interestingly, the TAR net can be applied to sequences with long-range dependence since the dimension along the sequential order is reduced. Theoretical studies show that the TAR net improves the learning efficiency, and requires much fewer samples for model training. Experiments on synthetic and real-world datasets demonstrate the promising performance of the proposed compact network.

Includes members:
  Member "tensor decomposition for compressing recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "compressing recurrent neural networks with tensor ring for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structured pruning of recurrent neural networks through neuron selection")
  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 "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "conditional image generation with pixelcnn decoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic latent residual video prediction")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "compression of deep convolutional neural networks for fast and low power mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "widening and squeezing towards accurate and efficient qnns")

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)