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 to learn from noisy labeled data"
  Abstract: Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect. There exist many inexpensive data sources on the web, but they tend to contain inaccurate labels. Training on noisy labeled datasets causes performance degradation because DNNs can easily overfit to the label noise. To overcome this problem, we propose a noise-tolerant training algorithm, where a meta-learning update is performed prior to conventional gradient update. The proposed meta-learning method simulates actual training by generating synthetic noisy labels, and train the model such that after one gradient update using each set of synthetic noisy labels, the model does not overfit to the specific noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method compared to several state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-4 (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"
    - "how does disagreement help generalization against label corruption"
    - "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"

Connected through hyperedge HE-5 (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-6 (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"
    - "how does disagreement help generalization against label corruption"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "rectified meta learning from noisy labels for robust image based plant disease diagnosis"):
  with members:
    - "deep self learning from noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "learning from noisy web data with category level supervision"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-8 (induced by source paper "noiserank unsupervised label noise reduction with dependence models"):
  with members:
    - "deep self learning from noisy labels"
    - "billion scale semi supervised learning for image classification"
    - "are anchor points really indispensable in label noise learning"
    - "an effective label noise model for dnn text classification"
    - "unsupervised label noise modeling and loss correction"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"

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)