human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "enaet self trained ensemble autoencoding transformations for semi supervised learning"):
  Abstract: Deep neural networks have been successfully applied to many real-world applications. However, these successes rely heavily on large amounts of labeled data, which is expensive to obtain. Recently, Auto-Encoding Transformation (AET) and MixMatch have been proposed and achieved state-of-the-art results for unsupervised and semi-supervised learning, respectively. In this study, we train an Ensemble of Auto-Encoding Transformations (EnAET) to learn from both labeled and unlabeled data based on the embedded representations by decoding both spatial and non-spatial transformations. This distinguishes EnAET from conventional semi-supervised methods that focus on improving prediction consistency and confidence by different models on both unlabeled and labeled examples. In contrast, we propose to explore the role of self-supervised representations in semi-supervised learning under a rich family of transformations. Experiment results on CIFAR-10, CIFAR-100, SVHN and STL10 demonstrate that the proposed EnAET outperforms the state-of-the-art semi-supervised methods by significant margins. In particular, we apply the proposed method to extremely challenging scenarios with only 10 images per class, and show that EnAET can achieve an error rate of 9.35% on CIFAR-10 and 16.92% on SVHN. In addition, EnAET achieves the best result when compared with fully supervised learning using all labeled data with the same network architecture. The performance on CIFAR-10, CIFAR-100 and SVHN with a smaller network is even more competitive than the state-of-the-art of supervised learning methods based on a larger network. We also set a new performance record with an error rate of 1.99% on CIFAR-10 and 4.52% on STL10. The code and experiment records are released at this https URL.

Includes members:
  Member "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on semi self and unsupervised techniques in image classification")
  Member "cutmix regularization strategy to train strong classifiers with localizable features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "population based augmentation efficient learning of augmentation policy schedules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "fast autoaugment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "mixmatch a holistic approach to semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive ensemble learning")
  Member "there are many consistent explanations of unlabeled data why you should average":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proposal learning for semi supervised object detection")
  Member "proxylessnas direct neural architecture search on target task and hardware":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

Question: Please tell me which class the source paper 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)