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: "tensor regression networks with various low rank tensor approximations"
  Abstract: Tensor regression networks achieve high rate of compression of model parameters in multilayer perceptrons (MLP) while having slight impact on performances. Tensor regression layer imposes low-rank constraints on the tensor regression layer which replaces the flattening operation of traditional MLP. We investigate tensor regression networks using various low-rank tensor approximations, aiming to leverage the multi-modal structure of high dimensional data by enforcing efficient low-rank constraints. We provide a theoretical analysis giving insights on the choice of the rank parameters. We evaluated performance of proposed model with state-of-the-art deep convolutional models. For CIFAR-10 dataset, we achieved the compression rate of 0.018 with the sacrifice of accuracy less than 1%.

Connected through hyperedge HE-1 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning"):
  with members:
    - "random expert distillation imitation learning via expert policy support estimation"
    - "weight agnostic neural networks"
    - "scattering networks for hybrid representation learning"
    - "when to use parametric models in reinforcement learning"
    - "model based reinforcement learning for atari"
    - "kymatio scattering transforms in python"
    - "generative networks as inverse problems with scattering transforms"
    - "playing atari with six neurons"

Connected through hyperedge HE-2 (induced by source paper "robust deep networks with randomized tensor regression layers"):
  with members:
    - "randomized tensor ring decomposition and its application to large scale data reconstruction"
    - "regularization for deep learning a taxonomy"
    - "on the implicit bias of dropout"
    - "a practical randomized cp tensor decomposition"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "tensor regression networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "tensor contraction layers for parsimonious deep nets"

Connected through hyperedge HE-3 (induced by source paper "compression and interpretability of deep neural networks via tucker tensor layer from first principles to tensor valued back propagation"):
  with members:
    - "ada tucker compressing deep neural networks via adaptive dimension adjustment tucker decomposition"
    - "tensor regression networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tensorizing neural networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "tucker tensor layer in fully connected neural networks"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tensorizing neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "speech recognition with deep recurrent neural networks"

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)