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: "learning from binary labels with instance dependent corruption"
  Abstract: Suppose we have a sample of instances paired with binary labels corrupted by arbitrary instance- and label-dependent noise. With sufficiently many such samples, can we optimally classify and rank instances with respect to the noise-free distribution? We provide a theoretical analysis of this question, with three main contributions. First, we prove that for instance-dependent noise, any algorithm that is consistent for classification on the noisy distribution is also consistent on the clean distribution. Second, we prove that for a broad class of instance- and label-dependent noise, a similar consistency result holds for the area under the ROC curve. Third, for the latter noise model, when the noise-free class-probability function belongs to the generalised linear model family, we show that the Isotron can efficiently and provably learn from the corrupted sample.

Connected through hyperedge HE-1 (induced by source paper "learning from noisy similar and dissimilar data"):
  with members:
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "a simple yet effective baseline for robust deep learning with noisy labels"):
  with members:
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "autoaugment learning augmentation policies from data"
    - "learning to reweight examples for robust deep learning"
    - "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"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-3 (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"
    - "masking a new perspective of noisy supervision"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-4 (induced by source paper "confidence scores make instance dependent label noise learning possible"):
  with members:
    - "learning with bad training data via iterative trimmed loss minimization"
    - "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"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "loss factorization weakly supervised learning and label noise robustness"

Connected through hyperedge HE-5 (induced by source paper "lead2gold towards exploiting the full potential of noisy transcriptions for speech recognition"):
  with members:
    - "active and semi supervised learning in asr benefits on the acoustic and language models"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "a fully differentiable beam search decoder"
    - "wav2letter the fastest open source speech recognition system"
    - "label noise robust generative adversarial networks"
    - "letter based speech recognition with gated convnets"
    - "improving lstm ctc based asr performance in domains with limited training data"
    - "wav2letter an end to end convnet based speech recognition system"

Connected through hyperedge HE-6 (induced by source paper "using trusted data to train deep networks on labels corrupted by severe noise"):
  with members:
    - "learning to reweight examples for robust deep learning"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "on calibration of modern neural networks"
    - "certified defenses for data poisoning attacks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"
    - "sgdr stochastic gradient descent with restarts"
    - "learning from untrusted data"

Connected through hyperedge HE-7 (induced by source paper "learning with bad training data via iterative trimmed loss minimization"):
  with members:
    - "high dimensional robust sparse regression"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

Connected through hyperedge HE-8 (induced by source paper "decoupling when to update from how to update"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "training convolutional networks with noisy labels"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "boosting in the presence of label noise"
    - "identifying mislabeled training data"

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)