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 to robust deep learning with noisy labels"
  Abstract: Recent studies reveal that deep neural networks gradually memorize individual data while fitting distributions of data. Hence, when facing noisy labels, all existing methods inevitably suffer from generalization degeneration and have to be early stopped. In this paper, we propose Pumpout as a meta approach to learning with noisy labels and an alternative to early stopping. Pumpout comes from sample selection and goes beyond: in every mini-batch, it uses gradient decent on good data, while it uses scaled gradient ascent on bad data rather than drops those data, where the goodness and badness are w.r.t. a base learning method. It is advantageous over early stopping, since it can continue to fit distributions of data and it has the ability of actively forgetting individual data that is memorized by mistakes. We demonstrate via experiments that Pumpout robustifies two representative base learning methods, and the performance boost is often significant.

Connected through hyperedge HE-1 (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"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning for classical japanese literature"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "positive unlabeled learning with non negative risk estimator"
    - "decoupled weight decay regularization"

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)