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: "learning from noisy labels by regularized estimation of annotator confusion"
  Abstract: The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground truth limits the accuracy of learning algorithms in the presence of strong disagreement. This problem is critical for applications in domains such as medical imaging where both the annotation cost and inter-observer variability are high. In this work, we present a method for simultaneously learning the individual annotator model and the underlying true label distribution, using only noisy observations. Each annotator is modeled by a confusion matrix that is jointly estimated along with the classifier predictions. We propose to add a regularization term to the loss function that encourages convergence to the true annotator confusion matrix. We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators. Despite the simplicity of the idea, experiments on image classification tasks with both simulated and real labels show that our method either outperforms or performs on par with the state-of-the-art methods and is capable of estimating the skills of annotators even with a single label available per image.

Connected through hyperedge HE-1 (induced by source paper "dividemix learning with noisy labels as semi supervised learning"):
  with members:
    - "robust learning from untrusted sources"
    - "how does disagreement help generalization against label corruption"
    - "unsupervised label noise modeling and loss correction"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "multi class classification from noisy similarity labeled data"):
  with members:
    - "deep self learning from noisy labels"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "are anchor points really indispensable in label noise learning"
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "noise tolerant paradigm for training face recognition cnns"
    - "multi class classification without multi class labels"

Connected through hyperedge HE-3 (induced by source paper "distilling effective supervision from severe label noise"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "randaugment practical data augmentation with no separate search"
    - "nlnl negative learning for noisy labels"
    - "unsupervised label noise modeling and loss correction"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-4 (induced by source paper "ieg robust neural network training to tackle severe label noise"):
  with members:
    - "deep self learning from noisy labels"
    - "nlnl negative learning for noisy labels"
    - "unsupervised label noise modeling and loss correction"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "autoaugment learning augmentation policies from data"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-5 (induced by source paper "reg r cnn lesion detection and grading under noisy labels"):
  with members:
    - "development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer"
    - "retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "let s agree to disagree learning highly debatable multirater labelling"):
  with members:
    - "3d multirater rcnn for multimodal multiclass detection and characterisation of extremely small objects"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from noisy labels with distillation"

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)