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: "tucker tensor layer in fully connected neural networks"
  Abstract: We introduce the Tucker Tensor Layer (TTL), an alternative to the dense weight-matrices of the fully connected layers of feed-forward neural networks (NNs), to answer the long standing quest to compress NNs and improve their interpretability. This is achieved by treating these weight-matrices as the unfolding of a higher order weight-tensor. This enables us to introduce a framework for exploiting the multi-way nature of the weight-tensor in order to efficiently reduce the number of parameters, by virtue of the compression properties of tensor decompositions. The Tucker Decomposition (TKD) is employed to decompose the weight-tensor into a core tensor and factor matrices. We re-derive back-propagation within this framework, by extending the notion of matrix derivatives to tensors. In this way, the physical interpretability of the TKD is exploited to gain insights into training, through the process of computing gradients with respect to each factor matrix. The proposed framework is validated on synthetic data and on the Fashion-MNIST dataset, emphasizing the relative importance of various data features in training, hence mitigating the "black-box" issue inherent to NNs. Experiments on both MNIST and Fashion-MNIST illustrate the compression properties of the TTL, achieving a 66.63 fold compression whilst maintaining comparable performance to the uncompressed NN.

Connected through hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "a fair comparison of graph neural networks for graph classification"
    - "bayesian tensor factorisation for bottom up hidden tree markov models"
    - "a gentle introduction to deep learning for graphs"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "how powerful are graph neural networks"
    - "mutan multimodal tucker fusion for visual question answering"
    - "event representations with tensor based compositions"

Connected through hyperedge HE-2 (induced by source paper "supervised learning for non sequential data with the canonical polyadic decomposition"):
  with members:
    - "tensornetwork for machine learning"
    - "generalized tensor models for recurrent neural networks"
    - "wide compression tensor ring nets"
    - "expressive power of recurrent neural networks"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensorizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the expressive power of deep learning a tensor analysis"

Connected through hyperedge HE-3 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "bayesian tensorized neural networks with automatic rank selection"):
  with members:
    - "wide compression tensor ring nets"
    - "tensor regression networks"
    - "compression aware training of deep networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "long term forecasting using higher order tensor rnns"
    - "ultimate tensorization compressing convolutional and fc layers alike"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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.LG(Machine Learning)