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 with bad training data via iterative trimmed loss minimization"
  Abstract: In this paper, we study a simple and generic framework to tackle the problem of learning model parameters when a fraction of the training samples are corrupted. We first make a simple observation: in a variety of such settings, the evolution of training accuracy (as a function of training epochs) is different for clean and bad samples. Based on this we propose to iteratively minimize the trimmed loss, by alternating between (a) selecting samples with lowest current loss, and (b) retraining a model on only these samples. We prove that this process recovers the ground truth (with linear convergence rate) in generalized linear models with standard statistical assumptions. Experimentally, we demonstrate its effectiveness in three settings: (a) deep image classifiers with errors only in labels, (b) generative adversarial networks with bad training images, and (c) deep image classifiers with adversarial (image, label) pairs (i.e., backdoor attacks). For the well-studied setting of random label noise, our algorithm achieves state-of-the-art performance without having access to any a-priori guaranteed clean samples.

Connected through hyperedge HE-1 (induced by source paper "dividemix learning with noisy labels as semi supervised learning"):
  with members:
    - "robust learning from untrusted sources"
    - "how does disagreement help generalization against label corruption"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "on defending against label flipping attacks on malware detection systems"
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "representer point selection for explaining deep neural networks"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-3 (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"
    - "self learning to filter noisy labels with self ensembling"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "image recognition from raw labels collected without annotators"):
  with members:
    - "a simple yet effective baseline for robust deep learning with noisy labels"
    - "synthetic vs real deep learning on controlled noise"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "limited gradient descent learning with noisy labels"
    - "deep learning is robust to massive label noise"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "robust training with ensemble consensus"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "using pre training can improve model robustness and uncertainty"
    - "the devil of face recognition is in the noise"
    - "deep learning is robust to massive label noise"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-7 (induced by source paper "leveraging inductive bias of neural networks for learning without explicit human annotations"):
  with members:
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "limited gradient descent learning with noisy labels"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "data valuation using reinforcement learning"):
  with members:
    - "robust learning at noisy labeled medical images applied to skin lesion classification"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "learning to transfer learn"
    - "uncertainty based detection and relabeling of noisy image labels"
    - "an empirical model of large batch training"
    - "choicenet robust learning by revealing output correlations"
    - "domain adaptive transfer learning with specialist models"
    - "an empirical study of example forgetting during deep neural network 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.LG(Machine Learning)