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 to reweight examples for robust deep learning"
  Abstract: Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.

Connected through hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-2 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-3 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning"):
  with members:
    - "a study on action detection in the wild"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "dynamic curriculum learning for imbalanced data classification"
    - "decoupling representation and classifier for long tailed recognition"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "meta weight net learning an explicit mapping for sample weighting"

Connected through hyperedge HE-4 (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"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-5 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-6 (induced by source paper "towards noise resistant object detection with noisy annotations"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "training object detectors with noisy data"
    - "unsupervised label noise modeling and loss correction"
    - "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"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"

Connected through hyperedge HE-7 (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-8 (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"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "masking a new perspective of noisy supervision"

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)