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: "the devil of face recognition is in the noise"
  Abstract: The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: 1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. 3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. 4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "explainable ai for trees from local explanations to global understanding"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-2 (induced by source paper "asymmetric rejection loss for fairer face recognition"):
  with members:
    - "unknown identity rejection loss utilizing unlabeled data for face recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "exploring disentangled feature representation beyond face identification"

Connected through hyperedge HE-3 (induced by source paper "how does gender balance in training data affect face recognition accuracy"):
  with members:
    - "analysis of gender inequality in face recognition accuracy"
    - "does face recognition accuracy get better with age deep face matchers say no"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "probabilistic face embeddings"
    - "characterizing the variability in face recognition accuracy relative to race"
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "correlation congruence for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "arcface additive angular margin loss for deep face recognition"
    - "large scale distributed neural network training through online distillation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "in defense of the triplet loss again learning robust person re identification with fast approximated triplet loss and label distillation"):
  with members:
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "calibrated domain invariant learning for highly generalizable large scale re identification"
    - "not all samples are created equal deep learning with importance sampling"
    - "support neighbor loss for person re identification"
    - "modality distillation with multiple stream networks for action recognition"
    - "iterative learning with open set noisy labels"
    - "in defense of the triplet loss for person re identification"
    - "margin sample mining loss a deep learning based method for person re identification"

Connected through hyperedge HE-6 (induced by source paper "noise tolerant paradigm for training face recognition cnns"):
  with members:
    - "deep learning is robust to massive label noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "ring loss convex feature normalization for face recognition"
    - "learning to reweight examples for robust deep learning"
    - "masking a new perspective of noisy supervision"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-7 (induced by source paper "mis classified vector guided softmax loss for face recognition"):
  with members:
    - "a high efficiency framework for constructing large scale face parsing benchmark"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "support vector guided softmax loss for face recognition"
    - "virtual class enhanced discriminative embedding learning"

Connected through hyperedge HE-8 (induced by source paper "wasserstein adversarial regularization war on label noise"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "how does disagreement help generalization against label corruption"
    - "adversarial robustness via adversarial label smoothing"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with 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)