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: "mnist c a robustness benchmark for computer vision"
  Abstract: We introduce the MNIST-C dataset, a comprehensive suite of 15 corruptions applied to the MNIST test set, for benchmarking out-of-distribution robustness in computer vision. Through several experiments and visualizations we demonstrate that our corruptions significantly degrade performance of state-of-the-art computer vision models while preserving the semantic content of the test images. In contrast to the popular notion of adversarial robustness, our model-agnostic corruptions do not seek worst-case performance but are instead designed to be broad and diverse, capturing multiple failure modes of modern models. In fact, we find that several previously published adversarial defenses significantly degrade robustness as measured by MNIST-C. We hope that our benchmark serves as a useful tool for future work in designing systems that are able to learn robust feature representations that capture the underlying semantics of the input.

Connected through hyperedge HE-1 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"

Connected through hyperedge HE-2 (induced by source paper "detecting and diagnosing adversarial images with class conditional capsule reconstructions"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial attacks and defences competition"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

Connected through hyperedge HE-3 (induced by source paper "confidence calibrated adversarial training generalizing to unseen attacks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "square attack a query efficient black box adversarial attack via random search"
    - "adversarial robustness against the union of multiple perturbation models"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "playing the game of universal adversarial perturbations"

Connected through hyperedge HE-4 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "conditional generative models are not robust"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "humans can decipher adversarial images"
    - "deep predictive coding network for object recognition"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

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)