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: "pumpout a meta approach for robustly training deep neural networks with noisy labels"
  Abstract: It is challenging to train deep neural networks robustly on the industrial-level data, since labels of such data are heavily noisy, and their label generation processes are normally agnostic. To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations. Then, we may employ early stopping or the small-loss trick to train them on selected instances. However, in such training procedures, deep neural networks inevitably memorize some noisy labels, which will degrade their generalization. In this paper, we propose a meta algorithm called Pumpout to overcome the problem of memorizing noisy labels. By using scaled stochastic gradient ascent, Pumpout actively squeezes out the negative effects of noisy labels from the training model, instead of passively forgetting these effects. We leverage Pumpout to upgrade two representative methods: MentorNet and Backward Correction. Empirical results on benchmark datasets demonstrate that Pumpout can significantly improve the robustness of representative methods.

Connected through hyperedge HE-1 (induced by source paper "does label smoothing mitigate label noise"):
  with members:
    - "regularization via structural label smoothing"
    - "are anchor points really indispensable in label noise learning"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "masking a new perspective of noisy supervision"
    - "on calibration of modern neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "do we need zero training loss after achieving zero training error"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "deep double descent where bigger models and more data hurt"
    - "pumpout a meta approach to robust deep learning with noisy labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning for classical japanese literature"
    - "positive unlabeled learning with non negative risk estimator"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"):
  with members:
    - "classification from positive unlabeled and biased negative data"
    - "dimensionality driven learning with noisy labels"
    - "positive unlabeled learning with non negative risk estimator"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "generative adversarial positive unlabelled learning"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "mixture proportion estimation via kernel embedding of distributions"

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.LG(Machine Learning)