human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "compressing recurrent neural networks with tensor ring for action recognition"
  Abstract: Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs unscalable and difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TR-LSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor train LSTM and other state-of-the-art competitors.

Connected through hyperedge HE-1 (induced by source paper "structured pruning of recurrent neural networks through neuron selection"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "block sparse recurrent neural networks"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning intrinsic sparse structures within long short term memory"
    - "regularizing and optimizing lstm language models"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep minimax probability machine"):
  with members:
    - "sparse dnns with improved adversarial robustness"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "extending defensive distillation"
    - "learning compact recurrent neural networks with block term tensor decomposition"
    - "end to end learning for self driving cars"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-3 (induced by source paper "compact autoregressive network"):
  with members:
    - "tensor decomposition for compressing recurrent neural network"
    - "learning compact recurrent neural networks with block term tensor decomposition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "conditional image generation with pixelcnn decoders"
    - "wavenet a generative model for raw audio"
    - "tensorizing neural networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "rethinking the inception architecture for computer vision"

Question: Please tell me which class the center node 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.CV(Computer Vision and Pattern Recognition)