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 generative adversarial nets"
  Abstract: Generative Adversarial Network (GAN) and its variants demonstrate state-of-the-art performance in the class of generative models. To capture higher dimensional distributions, the common learning procedure requires high computational complexity and large number of parameters. In this paper, we present a new generative adversarial framework by representing each layer as a tensor structure connected by multilinear operations, aiming to reduce the number of model parameters by a large factor while preserving the quality of generalized performance. To learn the model, we develop an efficient algorithm by alternating optimization of the mode connections. Experimental results demonstrate that our model can achieve high compression rate for model parameters up to 40 times as compared to the existing GAN.

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"
    - "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"
    - "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 "higher dimension tensor completion via low rank tensor ring decomposition"):
  with members:
    - "low rank tensor completion for multiway visual data"
    - "wide compression tensor ring nets"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "learning efficient tensor representations with ring structure networks"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor ring decomposition"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-3 (induced by source paper "tensorly tensor learning in python"):
  with members:
    - "tensor regression networks"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "tensorizing neural networks"
    - "tensor vs matrix methods robust tensor decomposition under block sparse perturbations"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "on the expressive power of deep learning a tensor analysis"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "api design for machine learning software experiences from the scikit learn project"

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)