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: "deep self learning from noisy labels"
  Abstract: ConvNets achieve good results when training from clean data, but learning from noisy labels significantly degrades performances and remains challenging. Unlike previous works constrained by many conditions, making them infeasible to real noisy cases, this work presents a novel deep self-learning framework to train a robust network on the real noisy datasets without extra supervision. The proposed approach has several appealing benefits. (1) Different from most existing work, it does not rely on any assumption on the distribution of the noisy labels, making it robust to real noises. (2) It does not need extra clean supervision or accessorial network to help training. (3) A self-learning framework is proposed to train the network in an iterative end-to-end manner, which is effective and efficient. Extensive experiments in challenging benchmarks such as Clothing1M and Food101-N show that our approach outperforms its counterparts in all empirical settings.

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:
    - "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"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "improving generalization by controlling label noise information in neural network weights"):
  with members:
    - "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"
    - "measuring the intrinsic dimension of objective landscapes"

Connected through hyperedge HE-3 (induced by source paper "multi class classification from noisy similarity labeled data"):
  with members:
    - "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"
    - "learning from noisy labels by regularized estimation of annotator confusion"

Connected through hyperedge HE-4 (induced by source paper "rectified meta learning from noisy labels for robust image based plant disease diagnosis"):
  with members:
    - "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"
    - "learning to learn from noisy labeled data"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-5 (induced by source paper "noiserank unsupervised label noise reduction with dependence models"):
  with members:
    - "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"
    - "learning to learn from noisy labeled data"

Connected through hyperedge HE-6 (induced by source paper "towards robust learning with different label noise distributions"):
  with members:
    - "are anchor points really indispensable in label noise learning"
    - "accelerating deep learning by focusing on the biggest losers"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "nlnl negative learning for noisy labels"
    - "unsupervised label noise modeling and loss correction"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"

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"
    - "symmetric cross entropy for robust learning with 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 "imae for noise robust learning mean absolute error does not treat examples equally and gradient magnitude s variance matters"):
  with members:
    - "ranked list loss for deep metric learning"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "id aware quality for set based person re identification"
    - "joint optimization framework for learning with noisy labels"
    - "smooth loss functions for deep top k classification"
    - "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"

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)