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: "apac augmented pattern classification with neural networks"
  Abstract: Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recently invented stochastic regularization techniques on the CIFAR-10 dataset.

Connected through hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"

Connected through hyperedge HE-2 (induced by source paper "the value of nullspace tuning using partial label information"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"
    - "three mechanisms of weight decay regularization"
    - "inter scanner harmonization of high angular resolution dw mri using null space deep learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "a branching and merging convolutional network with homogeneous filter capsules"):
  with members:
    - "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks"
    - "rmdl random multimodel deep learning for classification"
    - "averaging weights leads to wider optima and better generalization"
    - "random erasing data augmentation"
    - "dynamic routing between capsules"
    - "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
    - "densely connected convolutional networks"
    - "batch normalized maxout network in network"

Connected through hyperedge HE-4 (induced by source paper "stochastic optimization of plain convolutional neural networks with simple methods"):
  with members:
    - "smart augmentation learning an optimal data augmentation strategy"
    - "an analysis of deep neural network models for practical applications"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep image scaling up image recognition"
    - "efficient object localization using convolutional networks"
    - "deeppose human pose estimation via deep neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "ensemble learning in cnn augmented with fully connected subnetworks"):
  with members:
    - "rmdl random multimodel deep learning for classification"
    - "autoaugment learning augmentation policies from data"
    - "random erasing data augmentation"
    - "mixup beyond empirical risk minimization"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"):
  with members:
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-7 (induced by source paper "learning data augmentation strategies for object detection"):
  with members:
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "fast autoaugment"
    - "a fourier perspective on model robustness in computer vision"
    - "metaanchor learning to detect objects with customized anchors"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-8 (induced by source paper "randaugment practical automated data augmentation with a reduced search space"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"
    - "fast autoaugment"
    - "do imagenet classifiers generalize to imagenet"

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)