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: "unsupervised feature learning by augmenting single images"
  Abstract: When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce overfitting and increase the performance of the algorithm. In this paper we investigate if it is possible to use data augmentation as the main component of an unsupervised feature learning architecture. To that end we sample a set of random image patches and declare each of them to be a separate single-image surrogate class. We then extend these trivial one-element classes by applying a variety of transformations to the initial 'seed' patches. Finally we train a convolutional neural network to discriminate between these surrogate classes. The feature representation learned by the network can then be used in various vision tasks. We find that this simple feature learning algorithm is surprisingly successful, achieving competitive classification results on several popular vision datasets (STL-10, CIFAR-10, Caltech-101).

Connected through hyperedge HE-1 (induced by source paper "augmentor an image augmentation library for machine learning"):
  with members:
    - "deep image scaling up image recognition"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"
    - "some improvements on deep convolutional neural network based image classification"

Connected through hyperedge HE-2 (induced by source paper "how well can a cnn marginalize simple nuisances it is designed for"):
  with members:
    - "what makes for effective detection proposals"
    - "fast r cnn"
    - "on invariance and selectivity in representation learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "an empirical evaluation of current convolutional architectures ability to manage nuisance location and scale variability"):
  with members:
    - "what makes for effective detection proposals"
    - "fast r cnn"
    - "on invariance and selectivity in representation learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "flip rotate pooling convolution and split dropout on convolution neural networks for image classification"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"
    - "some improvements on deep convolutional neural network based image classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "dreaming more data class dependent distributions over diffeomorphisms for learned data augmentation"):
  with members:
    - "attentional neural network feature selection using cognitive feedback"
    - "locally scale invariant convolutional neural networks"
    - "semi supervised learning with deep generative models"
    - "depth map prediction from a single image using a multi scale deep network"
    - "locally orderless registration"
    - "learning invariant representations with local transformations"

Connected through hyperedge HE-6 (induced by source paper "domain size pooling in local descriptors dsp sift"):
  with members:
    - "dense correspondences across scenes and scales"
    - "visual scene representations contrast scaling and occlusion"
    - "return of the devil in the details delving deep into convolutional nets"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "on the design and analysis of multiple view descriptors"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"
    - "steps towards a theory of visual information active perception signal to symbol conversion and the interplay between sensing and control"

Connected through hyperedge HE-7 (induced by source paper "descriptor matching with convolutional neural networks a comparison to sift"):
  with members:
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "committees of deep feedforward networks trained with few data"):
  with members:
    - "an analysis of the connections between layers of deep neural networks"

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)