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: "self learning to filter noisy labels with self ensembling"
  Abstract: Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training. Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels. For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs. We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch. While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss. We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios. It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.

Connected through hyperedge HE-1 (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"
    - "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"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

Connected through hyperedge HE-2 (induced by source paper "self adaptive training beyond empirical risk minimization"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "selectivenet a deep neural network with an integrated reject option"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a"):
  with members:
    - "named entity recognition with partially annotated training data"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "low resource name tagging learned with weakly labeled data"
    - "cloze driven pretraining of self attention networks"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "feature dependent confusion matrices for low resource ner labeling with noisy labels"
    - "massively multilingual transfer for ner"
    - "handling noisy labels for robustly learning from self training data for low resource sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "trust your model iterative label improvement and robust training by confidence based filtering and dataset partitioning"):
  with members:
    - "deep semantic segmentation of natural and medical images a review"
    - "training object detectors with noisy data"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "robust learning under label noise with iterative noise filtering"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "learning to reweight examples for robust deep learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "understanding deep learning requires rethinking generalization"

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.CV(Computer Vision and Pattern Recognition)