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: "cp decomposition with tensor power method for convolutional neural networks compression"
  Abstract: Convolutional Neural Networks (CNNs) has shown a great success in many areas including complex image classification tasks. However, they need a lot of memory and computational cost, which hinders them from running in relatively low-end smart devices such as smart phones. We propose a CNN compression method based on CP-decomposition and Tensor Power Method. We also propose an iterative fine tuning, with which we fine-tune the whole network after decomposing each layer, but before decomposing the next layer. Significant reduction in memory and computation cost is achieved compared to state-of-the-art previous work with no more accuracy loss.

Connected through hyperedge HE-1 (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"
    - "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"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning multiple visual domains with residual adapters"

Connected through hyperedge HE-4 (induced by source paper "efficient n dimensional convolutions via higher order factorization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor contraction layers for parsimonious deep nets"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "tensorly tensor learning in python"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "efficient neural network compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "automatic rank selection for high speed convolutional neural network"
    - "efficient architecture search by network transformation"
    - "hierarchical representations for efficient architecture search"
    - "thinet a filter level pruning method for deep neural network compression"
    - "coordinating filters for faster deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "rank selection of cp decomposed convolutional layers with variational bayesian matrix factorization"):
  with members:
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "modeling and propagating cnns in a tree structure for visual tracking"
    - "you only look once unified real time object detection"
    - "learning both weights and connections for efficient neural networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "accelerating very deep convolutional networks for classification and detection"

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)