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: "tensorly tensor learning in python"
  Abstract: Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed \emph{TensorLy}, a high-level API for tensor methods and deep tensorized neural networks in Python. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and seamlessly integrates with them. Its BSD license makes it suitable for both academic and commercial applications. TensorLy's backend system allows users to perform computations with NumPy, MXNet, PyTorch, TensorFlow and CuPy. They can be scaled on multiple CPU or GPU machines. In addition, using the deep-learning frameworks as backend allows users to easily design and train deep tensorized neural networks. TensorLy is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "applying tensor decomposition to image for robustness against adversarial attack"):
  with members:
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "multiscale analysis for higher order tensors"
    - "generating long term trajectories using deep hierarchical networks"
    - "tensor regression meets gaussian processes"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "why should i trust you explaining the predictions of any classifier"
    - "parallel tensor compression for large scale scientific data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "tensorshield tensor based defense against adversarial attacks on images"):
  with members:
    - "the efficacy of shield under different threat models"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "t net parametrizing fully convolutional nets with a single high order tensor"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor contraction layers for parsimonious deep nets"
    - "tensorizing generative adversarial nets"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "matrix and tensor decompositions for training binary neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi scale structure aware network for human pose estimation"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "wrpn training and inference using wide reduced precision networks"
    - "tensor regression networks"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "incremental multi domain learning with network latent tensor factorization"):
  with members:
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning to generalize meta learning for domain generalization"
    - "incremental learning through deep adaptation"
    - "adversarial discriminative domain adaptation"

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)