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: "co teaching robust training of deep neural networks with extremely noisy labels"
  Abstract: Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called Co-teaching for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.

Connected through hyperedge HE-1 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"):
  with members:
    - "deep self learning from noisy labels"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "when does label smoothing help"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "dual student breaking the limits of the teacher in semi supervised learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-2 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-3 (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-4 (induced by source paper "identifying mislabeled data using the area under the margin ranking"):
  with members:
    - "dividemix learning with noisy labels as semi supervised learning"
    - "convolutional networks with dense connectivity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "towards robust learning with different label noise distributions"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "unsupervised label noise modeling and loss correction"
    - "l_dmi an information theoretic noise robust loss function"

Connected through hyperedge HE-5 (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-6 (induced by source paper "semi supervised class discovery"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "continual unsupervised representation learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "revisiting self supervised visual representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-7 (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"
    - "how does disagreement help generalization against label corruption"
    - "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"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-8 (induced by source paper "network cooperation with progressive disambiguation for partial label learning"):
  with members:
    - "partial label learning with self guided retraining"
    - "learning with inadequate and incorrect supervision"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from ambiguously labeled face images"
    - "understanding deep learning requires rethinking generalization"
    - "adam a method for stochastic optimization"

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)