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: "sharing residual units through collective tensor factorization in deep neural networks"
  Abstract: Residual units are wildly used for alleviating optimization difficulties when building deep neural networks. However, the performance gain does not well compensate the model size increase, indicating low parameter efficiency in these residual units. In this work, we first revisit the residual function in several variations of residual units and demonstrate that these residual functions can actually be explained with a unified framework based on generalized block term decomposition. Then, based on the new explanation, we propose a new architecture, Collective Residual Unit (CRU), which enhances the parameter efficiency of deep neural networks through collective tensor factorization. CRU enables knowledge sharing across different residual units using shared factors. Experimental results show that our proposed CRU Network demonstrates outstanding parameter efficiency, achieving comparable classification performance to ResNet-200 with the model size of ResNet-50. By building a deeper network using CRU, we can achieve state-of-the-art single model classification accuracy on ImageNet-1k and Places365-Standard benchmark datasets. (Code and trained models are available on GitHub)

Connected through hyperedge HE-1 (induced by source paper "convolutional neural networks with transformed input based on robust tensor network decomposition"):
  with members:
    - "interpretable convolutional neural networks via feedforward design"
    - "spatially transformed adversarial examples"
    - "sok security and privacy in machine learning"
    - "robustness of rotation equivariant networks to adversarial perturbations"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "on data driven saak transform"

Connected through hyperedge HE-2 (induced by source paper "iteratively training look up tables for network quantization"):
  with members:
    - "differentiable quantization of deep neural networks"
    - "searching for mobilenetv3"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learned step size quantization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-3 (induced by source paper "image super resolution via dual state recurrent networks"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "dual path networks"
    - "accelerating the super resolution convolutional neural network"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "deep networks with stochastic depth"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "learning compact recurrent neural networks with block term tensor decomposition"):
  with members:
    - "tensor regression networks"
    - "tensor train recurrent neural networks for video classification"
    - "compressing recurrent neural network with tensor train"
    - "tensorial recurrent neural networks for longitudinal data analysis"
    - "long term forecasting using higher order tensor rnns"
    - "learning efficient tensor representations with ring structure networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "dual path networks"):
  with members:
    - "memory efficient implementation of densenets"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "higher order recurrent neural networks"
    - "identity mappings in deep residual networks"
    - "places an image database for deep scene understanding"

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.CV(Computer Vision and Pattern Recognition)