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: "adversarial vulnerability for any classifier"
  Abstract: Despite achieving impressive and often superhuman performance on multiple benchmarks, state-of-the-art deep networks remain highly vulnerable to perturbations: adding small, imperceptible, adversarial perturbations can lead to very high error rates. Provided the data distribution is defined using a generative model mapping latent vectors to datapoints in the distribution, we prove that no classifier can be robust to adversarial perturbations when the latent space is sufficiently large and the generative model sufficiently smooth. Under the same conditions, we prove the existence of adversarial perturbations that transfer well across different models with small risk. We conclude the paper with experiments validating the theoretical bounds.

Connected through hyperedge HE-1 (induced by source paper "utilizing network properties to detect erroneous inputs"):
  with members:
    - "looking ahead anticipating pedestrians crossing with future frames prediction"
    - "adversarial examples are a natural consequence of test error in noise"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "natural adversarial examples"
    - "selectivenet a deep neural network with an integrated reject option"
    - "improved adversarial robustness by reducing open space risk via tent activations"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"

Connected through hyperedge HE-2 (induced by source paper "adversarial data encryption"):
  with members:
    - "convolutional networks with dense connectivity"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial music real world audio adversary against wake word detection system"

Connected through hyperedge HE-3 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models"):
  with members:
    - "adversarially robust learning could leverage computational hardness"
    - "lower bounds for adversarially robust pac learning"
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"

Connected through hyperedge HE-4 (induced by source paper "adversarial training is a form of data dependent operator norm regularization"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study"):
  with members:
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "cross entropy loss and low rank features have responsibility for adversarial examples"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "deep skinny neural networks are not universal approximators"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-6 (induced by source paper "understanding the intrinsic robustness of image distributions using conditional generative models"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "lower bounds on adversarial robustness from optimal transport"
    - "empirically measuring concentration fundamental limits on intrinsic robustness"
    - "theoretically principled trade off between robustness and accuracy"
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "mixtrain scalable training of formally robust neural networks"
    - "adversarial spheres"

Connected through hyperedge HE-7 (induced by source paper "randomization matters how to defend against strong adversarial attacks"):
  with members:
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "robust attacks against multiple classifiers"
    - "adversarial risk via optimal transport and optimal couplings"
    - "on evaluating adversarial robustness"
    - "theoretical evidence for adversarial robustness through randomization"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"

Connected through hyperedge HE-8 (induced by source paper "learning adversarially robust representations via worst case mutual information maximization"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "robust classification using robust feature augmentation"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "a spectral view of adversarially robust features"
    - "adversarial spheres"
    - "are adversarial examples inevitable"

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)