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 "ieg robust neural network training to tackle severe label noise"):
  Abstract: Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing methods suffer severely for training datasets with high noise ratios, making high-cost human labeling a necessity. Here we present a method to train neural networks in a way that is almost invulnerable to severe label noise by utilizing a tiny trusted set. Our method, named IEG, is based on three key insights: (i) Isolation of noisy labels, (ii) Escalation of useful supervision from mislabeled data, and (iii) Guidance from small trusted data. On CIFAR100 with a 40% uniform noise ratio and 10 trusted labeled data per class, our method achieves $80.2{\pm}0.3\%$ classification accuracy, only 1.4% higher error than a neural network trained without label noise. Moreover, increasing the noise ratio to 80%, our method still achieves a high accuracy of $75.5{\pm}0.2\%$, compared to the previous best 47.7%. Finally, our method sets new state of the art on various types of challenging label corruption types and levels and large-scale WebVision benchmarks.

Includes members:
  Member "deep self learning from noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "nlnl negative learning for noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from web data with self organizing memory module")
  Member "unsupervised label noise modeling and loss correction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "learning from noisy labels by regularized estimation of annotator confusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "mixmatch a holistic approach to semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive ensemble learning")
  Member "understanding and utilizing deep neural networks trained with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "a closer look at deep learning heuristics learning rate restarts warmup and distillation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

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)