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: "are labels required for improving adversarial robustness"
  Abstract: Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. This result is a key hurdle in the deployment of robust machine learning models in many real world applications where labeled data is expensive. Our main insight is that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models. Theoretically, we show that in a simple statistical setting, the sample complexity for learning an adversarially robust model from unlabeled data matches the fully supervised case up to constant factors. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Finally, we report an improvement of 4% over the previous state-of-the-art on CIFAR-10 against the strongest known attack by using additional unlabeled data from the uncurated 80 Million Tiny Images dataset. This demonstrates that our finding extends as well to the more realistic case where unlabeled data is also uncurated, therefore opening a new avenue for improving adversarial training.

Connected through hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-2 (induced by source paper "adversarial distributional training for robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "a direct approach to robust deep learning using adversarial networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "macer attack free and scalable robust training via maximizing certified radius"):
  with members:
    - "mma training direct input space margin maximization through adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-4 (induced by source paper "mma training direct input space margin maximization through adversarial training"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "on the effectiveness of low frequency perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "using pre training can improve model robustness and uncertainty"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "understanding self training for gradual domain adaptation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "understanding and mitigating the tradeoff between robustness and accuracy"
    - "confidence regularized self training"
    - "self training with noisy student improves imagenet classification"
    - "on learning invariant representation for domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "adversarial domain adaptation for stable brain machine interfaces"
    - "cross domain weakly supervised object detection through progressive domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "understanding and mitigating the tradeoff between robustness and accuracy"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "theoretically principled trade off between robustness and accuracy"
    - "unsupervised data augmentation for consistency training"
    - "a fourier perspective on model robustness in computer vision"
    - "generating natural language adversarial examples"
    - "adversarially robust generalization requires more data"
    - "exploring the landscape of spatial robustness"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"

Connected through hyperedge HE-7 (induced by source paper "self training with noisy student improves imagenet classification"):
  with members:
    - "using videos to evaluate image model robustness"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "robustness properties of facebook s resnext wsl models"
    - "billion scale semi supervised learning for image classification"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "natural adversarial examples"
    - "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"

Connected through hyperedge HE-8 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"
    - "selfie self supervised pretraining for image embedding"

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)