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: "how does disagreement help generalization against label corruption"
  Abstract: Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the "Update by Disagreement" strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co-teaching+ is much superior to many state-of-the-art methods in the robustness of trained models.

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"
    - "unsupervised label noise modeling and loss correction"
    - "learning from noisy labels by regularized estimation of annotator confusion"
    - "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 "towards noise resistant object detection with noisy annotations"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "training object detectors with noisy data"
    - "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"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "improving generalization by controlling label noise information in neural network weights"):
  with members:
    - "deep self learning from noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "where is the information in a deep neural network"
    - "unsupervised label noise modeling and loss correction"
    - "meta learning without memorization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "measuring the intrinsic dimension of objective landscapes"

Connected through hyperedge HE-4 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization"):
  with members:
    - "partial label learning with self guided retraining"
    - "learning with inadequate and incorrect supervision"
    - "are anchor points really indispensable in label noise learning"
    - "nlnl negative learning for noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-5 (induced by source paper "data selection for federated learning with relevant and irrelevant data at clients"):
  with members:
    - "advances and open problems in federated learning"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "leaf a benchmark for federated settings"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-6 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "laprop a better way to combine momentum with adaptive gradient"
    - "deep gamblers learning to abstain with portfolio theory"
    - "sgd on neural networks learns functions of increasing complexity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "selectivenet a deep neural network with an integrated reject option"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "al2 progressive activation loss for learning general representations in classification neural networks"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dimensionality driven learning with noisy labels"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "exploring generalization in deep learning"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-8 (induced by source paper "analysis of softmax approximation for deep classifiers under input dependent label noise"):
  with members:
    - "accurate uncertainty estimation and decomposition in ensemble learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "xception deep learning with depthwise separable convolutions"

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)