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: "auxiliary image regularization for deep cnns with noisy labels"
  Abstract: Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and those errors substantially hinder the learning of very accurate CNN models. In this work, we consider the problem of training a deep CNN model for image classification with mislabeled training samples - an issue that is common in real image data sets with tags supplied by amateur users. To solve this problem, we propose an auxiliary image regularization technique, optimized by the stochastic Alternating Direction Method of Multipliers (ADMM) algorithm, that automatically exploits the mutual context information among training images and encourages the model to select reliable images to robustify the learning process. Comprehensive experiments on benchmark data sets clearly demonstrate our proposed regularized CNN model is resistant to label noise in training data.

Connected through hyperedge HE-1 (induced by source paper "suppressing uncertainties for large scale facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "noise tolerant paradigm for training face recognition cnns"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "arcface additive angular margin loss for deep face recognition"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (induced by source paper "synthetic vs real deep learning on controlled noise"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "unsupervised label noise modeling and loss correction"
    - "using pre training can improve model robustness and uncertainty"
    - "robust neural machine translation with doubly adversarial inputs"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-3 (induced by source paper "meta weight net learning an explicit mapping for sample weighting"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "reptile a scalable metalearning algorithm"
    - "gradient agreement as an optimization objective for meta learning"
    - "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-4 (induced by source paper "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "videos as space time region graphs"
    - "step by step erasion one by one collection a weakly supervised temporal action detector"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal segment networks for action recognition in videos"
    - "multiple instance learning a survey of problem characteristics and applications"

Connected through hyperedge HE-5 (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-6 (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"
    - "masking a new perspective of noisy supervision"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "on first order meta learning algorithms"
    - "learning to teach with dynamic loss functions"
    - "gradient agreement as an optimization objective for meta learning"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-8 (induced by source paper "self learning to filter noisy labels with self ensembling"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "deep bilevel learning"
    - "deep learning is robust to massive label noise"
    - "anomaly detection with multiple hypotheses predictions"
    - "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"
    - "learning to reweight examples for robust deep 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.CV(Computer Vision and Pattern Recognition)