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: "sort second order response transform for visual recognition"
  Abstract: In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks. We propose a novel approach named Second-Order Response Transform (SORT), which appends element-wise product transform to the linear sum of a two-branch network module. A direct advantage of SORT is to facilitate cross-branch response propagation, so that each branch can update its weights based on the current status of the other branch. Moreover, SORT augments the family of transform operations and increases the nonlinearity of the network, making it possible to learn flexible functions to fit the complicated distribution of feature space. SORT can be applied to a wide range of network architectures, including a branched variant of a chain-styled network and a residual network, with very light-weighted modifications. We observe consistent accuracy gain on both small (CIFAR10, CIFAR100 and SVHN) and big (ILSVRC2012) datasets. In addition, SORT is very efficient, as the extra computation overhead is less than 5%.

Connected through hyperedge HE-1 (induced by source paper "rethinking normalization and elimination singularity in neural networks"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "a mean field theory of batch normalization"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "weight standardization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"

Connected through hyperedge HE-2 (induced by source paper "specnet spectral domain convolutional neural network"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "memory efficient implementation of densenets"
    - "wavelet convolutional neural networks for texture classification"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "universal approximation with quadratic deep networks"
    - "fuzzy logic interpretation of artificial neural networks"
    - "dense associative memory is robust to adversarial inputs"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "non linear convolution filters for cnn based learning"

Connected through hyperedge HE-4 (induced by source paper "neural rejuvenation improving deep network training by enhancing computational resource utilization"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "deep clustering for unsupervised learning of visual features"
    - "stacked cross attention for image text matching"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "deep co training for semi supervised image recognition"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-5 (induced by source paper "bounding box regression with uncertainty for accurate object detection"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "cornernet detecting objects as paired keypoints"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-6 (induced by source paper "multi scale spatially asymmetric recalibration for image classification"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "shake shake regularization"
    - "stacked hourglass networks for human pose estimation"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-7 (induced by source paper "global second order pooling convolutional networks"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "dropblock a regularization method for convolutional networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "multi scale location aware kernel representation for object detection"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non linear convolution filters for cnn based learning"
    - "non local neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep co training for semi supervised image recognition"):
  with members:
    - "multi scale spatially asymmetric recalibration for image classification"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "few shot image recognition by predicting parameters from activations"
    - "gradually updated neural networks for large scale image recognition"
    - "introspective classification with convolutional nets"
    - "single shot object detection with enriched semantics"
    - "adversarial examples for semantic segmentation and object 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.CV(Computer Vision and Pattern Recognition)