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: "masking a new perspective of noisy supervision"
  Abstract: It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. However, such estimation is practically difficult, due to either the indirect nature of two-step approaches, or not big enough data to afford end-to-end approaches. In this paper, we propose a human-assisted approach called Masking that conveys human cognition of invalid class transitions and naturally speculates the structure of the noise transition matrix. To this end, we derive a structure-aware probabilistic model incorporating a structure prior, and solve the challenges from structure extraction and structure alignment. Thanks to Masking, we only estimate unmasked noise transition probabilities and the burden of estimation is tremendously reduced. We conduct extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as well as the industrial-level Clothing1M with agnostic noise structure, and the results show that Masking can improve the robustness of classifiers significantly.

Connected through hyperedge HE-1 (induced by source paper "derivative manipulation for general example weighting"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "does label smoothing mitigate label noise"):
  with members:
    - "regularization via structural label smoothing"
    - "are anchor points really indispensable in label noise learning"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "on calibration of modern neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "towards mixture proportion estimation without irreducibility"):
  with members:
    - "dedpul method for mixture proportion estimation and positive unlabeled classification based on density estimation"
    - "are anchor points really indispensable in label noise learning"
    - "dedpul difference of estimated densities based positive unlabeled learning"
    - "mitigating overfitting in supervised classification from two unlabeled datasets a consistent risk correction approach"
    - "classification from pairwise similarity and unlabeled data"
    - "positive unlabeled learning with non negative risk estimator"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"

Connected through hyperedge HE-4 (induced by source paper "derivative manipulation for adjusting emphasis density function a general example weighting framework"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "noise tolerant paradigm for training face recognition cnns"):
  with members:
    - "the devil of face recognition is in the noise"
    - "deep learning is robust to massive label noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "ring loss convex feature normalization for face recognition"
    - "learning to reweight examples for robust deep learning"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-6 (induced by source paper "l_dmi an information theoretic noise robust loss function"):
  with members:
    - "max mig an information theoretic approach for joint learning from crowds"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "water from two rocks maximizing the mutual information"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-7 (induced by source paper "an effective label noise model for dnn text classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "recent trends in deep learning based natural language processing"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep learning from noisy image labels with quality embedding"
    - "toward robustness against label noise in training deep discriminative neural networks"

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:
    - "deep self learning from noisy labels"
    - "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"

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)