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: "wide compression tensor ring nets"
  Abstract: Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The trade-off is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability. Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly compress both the fully connected layers and the convolutional layers of deep neural networks. Our results show that our TR-Nets approach {is able to compress LeNet-5 by $11\times$ without losing accuracy}, and can compress the state-of-the-art Wide ResNet by $243\times$ with only 2.3\% degradation in {Cifar10 image classification}. Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resource-constrained devices such as smartphones, wearables, and IoT devices.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

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"
    - "tucker tensor layer in fully connected neural networks"
    - "generalized tensor models for recurrent neural networks"
    - "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 "an optimal transport framework for zero shot learning"):
  with members:
    - "improving textual network embedding with global attention via optimal transport"
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "zero shot kernel learning"

Connected through hyperedge HE-4 (induced by source paper "structured multi hashing for model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "searching for mobilenetv3"
    - "model compression by entropy penalized reparameterization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "non discriminative data or weak model on the relative importance of data and model resolution"
    - "measuring the intrinsic dimension of objective landscapes"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-5 (induced by source paper "einconv exploring unexplored tensor decompositions for convolutional neural networks"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "bayesian tensorized neural networks with automatic rank selection"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"):
  with members:
    - "higher dimension tensor completion via low rank tensor ring decomposition"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "tensor ring decomposition"
    - "tensorizing neural networks"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-8 (induced by source paper "tensor train neighborhood preserving embedding"):
  with members:
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "compressing recurrent neural network with tensor train"
    - "efficient low rank tensor ring completion"
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor ring decomposition"
    - "tensorizing 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)