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: "tensorizing neural networks"
  Abstract: Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep VGG networks we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times.

Connected through hyperedge HE-1 (induced by source paper "sparse weight activation training"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "swalp stochastic weight averaging in low precision training"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "per tensor fixed point quantization of the back propagation algorithm"

Connected through hyperedge HE-2 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "compression of convolutional neural networks for high performance imagematching tasks on mobile devices"):
  with members:
    - "sosnet second order similarity regularization for local descriptor learning"
    - "lf net learning local features from images"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-4 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "tucker tensor layer in fully connected neural networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "tensor networks for language modeling"):
  with members:
    - "singular value automata and approximate minimization"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "attention is all you need"
    - "controlling linguistic style aspects in neural language generation"
    - "language as a matrix product state"
    - "regularizing and optimizing lstm language models"
    - "tensor network language model"
    - "language design as information renormalization"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-7 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "spartan scalable parafac2 for large sparse data"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "simtensor a synthetic tensor data generator"
    - "high performance tensor contraction without transposition"
    - "the gap benchmark suite"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-8 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"

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)