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 "two cycle learning clustering based regularisation for deep semi supervised classification"):
  Abstract: This works addresses the challenge of classification with minimal annotations. Obtaining annotated data is time consuming, expensive and can require expert knowledge. As a result, there is an acceleration towards semi-supervised learning (SSL) approaches which utilise large amounts of unlabelled data to improve classification performance. The vast majority of SSL approaches have focused on implementing the \textit{low-density separation assumption}, in which the idea is that decision boundaries should lie in low density regions. However, they have implemented this assumption by treating the dataset as a set of individual attributes rather than as a global structure, which limits the overall performance of the classifier. Therefore, in this work, we go beyond this implementation and propose a novel SSL framework called two-cycle learning. For the first cycle, we use clustering based regularisation that allows for improved decision boundaries as well as features that generalises well. The second cycle is set as a graph based SSL that take advantages of the richer discriminative features of the first cycle to significantly boost the accuracy of generated pseudo-labels. We evaluate our two-cycle learning method extensively across multiple datasets, outperforming current approaches.

Includes members:
  Member "adversarial robustness may be at odds with simplicity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "semi supervised learning by label gradient alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning")
  Member "superpixel contracted graph based learning for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic spectral residual superpixels")
  Member "beyond supervised classification extreme minimal supervision with the graph 1 laplacian":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on defending against label flipping attacks on malware detection systems")
  Member "label propagation for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "deep metric transfer for label propagation with limited annotated data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition")
  Member "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")

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.LG(Machine Learning)