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: "identifying mislabeled training data"
  Abstract: This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30%. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.

Connected through hyperedge HE-1 (induced by source paper "improving training on noisy stuctured labels"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "deep learning is robust to massive label noise"
    - "learning to reweight examples for robust deep learning"
    - "imperfect segmentation labels how much do they matter"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "neural architectures for named entity recognition"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "an effective label noise model for dnn text classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "masking a new perspective of noisy supervision"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "recent trends in deep learning based natural language processing"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep learning from noisy image labels with quality embedding"

Connected through hyperedge HE-4 (induced by source paper "uncertainty based detection and relabeling of noisy image labels"):
  with members:
    - "unsupervised label noise modeling and loss correction"
    - "deep bilevel learning"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-5 (induced by source paper "product image recognition with guidance learning and noisy supervision"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "deep learning is robust to massive label noise"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-6 (induced by source paper "probabilistic end to end noise correction for learning with noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep label distribution learning with label ambiguity"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-7 (induced by source paper "auto annotation quality prediction for semi supervised learning with ensembles"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "snapshot ensembles train 1 get m for free"
    - "data distillation towards omni supervised learning"
    - "checkpoint ensembles ensemble methods from a single training process"
    - "mix and match tuning for self supervised semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "on modelling label uncertainty in deep neural networks automatic estimation of intra observer variability in 2d echocardiography quality assessment"):
  with members:
    - "a machine learning framework for automatic reference free quality assessment in mri"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"
    - "on calibration of modern neural networks"
    - "deep label distribution learning with label ambiguity"
    - "comparative study of cnn and rnn for natural language processing"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.AI(Artificial Intelligence)