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 "a non intrusive correction algorithm for classification problems with corrupted data"):
  Abstract: A novel correction algorithm is proposed for multi-class classification problems with corrupted training data. The algorithm is non-intrusive, in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction. The correction procedure can be coupled with any approximators, such as logistic regression, neural networks of various architectures, etc. When training dataset is sufficiently large, we prove that the corrected models deliver correct classification results as if there is no corruption in the training data. For datasets of finite size, the corrected models produce significantly better recovery results, compared to the models without the correction algorithm. All of the theoretical findings in the paper are verified by our numerical examples.

Includes members:
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "learning to reweight examples for robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "generalized cross entropy loss for training deep neural networks with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "using trusted data to train deep networks on labels corrupted by severe noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "certified defenses for data poisoning attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "learning from noisy singly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "mentornet learning data driven curriculum for very deep neural networks on corrupted labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "learning from noisy large scale datasets with minimal supervision":
    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)