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: "data augmentation using random image cropping and patching for deep cnns"
  Abstract: Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are rendering traditional data augmentation techniques insufficient. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image. Moreover, RICAP mixes the class labels of the four images, resulting in an advantage of the soft labels. We evaluated RICAP with current state-of-the-art CNNs (e.g., the shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup. RICAP achieves a new state-of-the-art test error of 2.19% on CIFAR-10. We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet, an image-caption retrieval task using Microsoft COCO, and other computer vision tasks.

Connected through hyperedge HE-1 (induced by source paper "understanding and enhancing mixed sample data augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "natural adversarial examples"
    - "fast autoaugment"
    - "data augmentation revisited rethinking the distribution gap between clean and augmented data"
    - "human uncertainty makes classification more robust"
    - "data augmentation by pairing samples for images classification"
    - "improved mixed example data augmentation"

Connected through hyperedge HE-2 (induced by source paper "data augmentation with mobius transformations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "enabling explainable fusion in deep learning with fuzzy integral neural networks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "data augmentation for object detection via progressive and selective instance switching"):
  with members:
    - "dynamic curriculum learning for imbalanced data classification"
    - "class balanced loss based on effective number of samples"
    - "learning to segment via cut and paste"
    - "data augmentation by pairing samples for images classification"
    - "on the importance of visual context for data augmentation in scene understanding"
    - "compositional gan learning conditional image composition"
    - "modeling visual context is key to augmenting object detection datasets"
    - "revisiting pre training an efficient training method for image classification"

Connected through hyperedge HE-4 (induced by source paper "dual active sampling on batch incremental active learning"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "cost effective active learning for deep image classification"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

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)