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 "when labelled data hurts deep semi supervised classification with the graph 1 laplacian"):
  Abstract: We consider the task of classifying when a significantly reduced amount of labelled data is available. This problem is of a great interest, in several real-world problems, as obtaining large amounts of labelled data is expensive and time consuming. We present a novel semi-supervised framework for multi-class classification that is based on the non-smooth $\ell_1$ norm of the normalised graph 1-Laplacian. Our transductive framework is framed under a novel functional with carefully selected class priors - that enforces a sufficiently smooth solution and strengthens the intrinsic relation between the labelled and unlabelled data. We provide theoretical results of our new optimisation model and show its connections with deep learning for handling large-scale datasets. We demonstrate through extensive experimental results on large datasets - CIFAR-10, CIFAR-100 and ChestX-Ray14 - that our method outperforms classic methods and readily competes with recent deep-learning approaches.

Includes members:
  Member "semi supervised learning by label gradient alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")
  Member "label propagation for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")
  Member "weakly supervised deep learning for thoracic disease classification and localization on chest x rays":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the graphnet zoo a plug and play framework for deep semi supervised classification")
  Member "comparison of deep learning approaches for multi label chest x ray classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the graphnet zoo a plug and play framework for deep semi supervised classification")
  Member "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays")
  Member "weakly supervised medical diagnosis and localization from multiple resolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "smooth neighbors on teacher graphs for semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")

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)