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: "symmetric cross entropy for robust learning with noisy labels"
  Abstract: Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes). Intuitively, CE requires an extra term to facilitate learning of hard classes, and more importantly, this term should be noise tolerant, so as to avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we propose the approach of \textbf{Symmetric cross entropy Learning} (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels. We provide a theoretical analysis of SL and also empirically show, on a range of benchmark and real-world datasets, that SL outperforms state-of-the-art methods. We also show that SL can be easily incorporated into existing methods in order to further enhance their performance.

Connected through hyperedge HE-1 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "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"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-2 (induced by source paper "derivative manipulation for general example weighting"):
  with members:
    - "nlnl negative learning for noisy labels"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "deep metric learning by online soft mining and class aware attention"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "masking a new perspective of noisy supervision"

Connected through hyperedge HE-3 (induced by source paper "self adaptive training beyond empirical risk minimization"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "self learning to filter noisy labels with self ensembling"
    - "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-4 (induced by source paper "learning adaptive loss for robust learning with noisy labels"):
  with members:
    - "curriculum loss robust learning and generalization against label corruption"
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "l_dmi an information theoretic noise robust loss function"
    - "learning surrogate losses"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "robust bi tempered logistic loss based on bregman divergences"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

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"
    - "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"
    - "learning from noisy labels by regularized estimation of annotator confusion"

Connected through hyperedge HE-6 (induced by source paper "derivative manipulation for adjusting emphasis density function a general example weighting framework"):
  with members:
    - "nlnl negative learning for noisy labels"
    - "improving mae against cce under label noise"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "deep metric learning by online soft mining and class aware attention"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-7 (induced by source paper "improving generalization and robustness with noisy collaboration in knowledge distillation"):
  with members:
    - "using videos to evaluate image model robustness"
    - "deep self learning from noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-8 (induced by source paper "distilling effective supervision from severe label noise"):
  with members:
    - "randaugment practical data augmentation with no separate search"
    - "nlnl negative learning for noisy labels"
    - "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"
    - "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)