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: "who said what modeling individual labelers improves classification"
  Abstract: Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label or to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computer-aided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels for training.

Connected through hyperedge HE-1 (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"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "deep decoder concise image representations from untrained non convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "weakly and semi supervised detection in medical imaging via deep dual branch net"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "optimizing region selection for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "a multi scale cnn and curriculum learning strategy for mammogram classification"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"

Connected through hyperedge HE-3 (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"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-4 (induced by source paper "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "how does disagreement help generalization against label corruption"
    - "on exact computation with an infinitely wide neural net"
    - "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"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-5 (induced by source paper "natural vocabulary emerges from free form annotations"):
  with members:
    - "guide me interacting with deep networks"
    - "learning to discover and localize visual objects with open vocabulary"
    - "open vocabulary phrase detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "context embedding networks"
    - "teaching machines to describe images via natural language feedback"
    - "open vocabulary scene parsing"

Connected through hyperedge HE-6 (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"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-7 (induced by source paper "learning to contextually aggregate multi source supervision for sequence labeling"):
  with members:
    - "crossweigh training named entity tagger from imperfect annotations"
    - "massively multilingual transfer for ner"
    - "multinomial adversarial networks for multi domain text classification"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "semi supervised sequence modeling with cross view training"
    - "neural adaptation layers for cross domain named entity recognition"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "learning from noisy labels by regularized estimation of annotator confusion"):
  with members:
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "learning from noisy singly labeled data"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "a survey on deep learning in medical image analysis"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "deep residual learning for image recognition"

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)