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 "roi regularization for semi supervised and supervised learning"):
  Abstract: We propose ROI regularization (ROIreg) as a semi-supervised learning method for image classification. ROIreg focuses on the maximum probability of a posterior probability distribution g(x) obtained when inputting an unlabeled data sample x into a convolutional neural network (CNN). ROIreg divides the pixel set of x into multiple blocks and evaluates, for each block, its contribution to the maximum probability. A masked data sample x_ROI is generated by replacing blocks with relatively small degrees of contribution with random images. Then, ROIreg trains CNN so that g(x_ROI ) does not change as much as possible from g(x). Therefore, ROIreg can be said to refine the classification ability of CNN more. On the other hand, Virtual Adverserial Training (VAT), which is an excellent semi-supervised learning method, generates data sample x_VAT by perturbing x in the direction in which g(x) changes most. Then, VAT trains CNN so that g(x_VAT ) does not change from g(x) as much as possible. Therefore, VAT can be said to be a method to improve CNN's weakness. Thus, ROIreg and VAT have complementary training effects. In fact, the combination of VAT and ROIreg improves the results obtained when using VAT or ROIreg alone. This combination also improves the state-of-the-art on "SVHN with and without data augmentation" and "CIFAR-10 without data augmentation". We also propose a method called ROI augmentation (ROIaug) as a method to apply ROIreg to data augmentation in supervised learning. However, the evaluation function used there is different from the standard cross-entropy. ROIaug improves the performance of supervised learning for both SVHN and CIFAR-10. Finally, we investigate the performance degradation of VAT and VAT+ROIreg when data samples not belonging to classification classes are included in unlabeled data.

Includes members:
  Member "there are many consistent explanations of unlabeled data why you should average":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "proposal learning for semi supervised object detection")
  Member "averaging weights leads to wider optima and better generalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "realistic evaluation of deep semi supervised learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "random erasing data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "good semi supervised learning that requires a bad gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial code learning for image generation")
  Member "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "adversarial dropout for supervised and semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")
  Member "colorization as a proxy task for visual understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rgb based semantic segmentation using self supervised depth pre training")

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)