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: "on the geometry of adversarial examples"
  Abstract: Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of adversarial examples. In particular, we highlight the importance of codimension: for low-dimensional data manifolds embedded in high-dimensional space there are many directions off the manifold in which to construct adversarial examples. Adversarial examples are a natural consequence of learning a decision boundary that classifies the low-dimensional data manifold well, but classifies points near the manifold incorrectly. Using our geometric framework we prove (1) a tradeoff between robustness under different norms, (2) that adversarial training in balls around the data is sample inefficient, and (3) sufficient sampling conditions under which nearest neighbor classifiers and ball-based adversarial training are robust.

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 "robustness of bayesian neural networks to gradient based attacks"):
  with members:
    - "statistical guarantees for the robustness of bayesian neural networks"
    - "uncertainty quantification with statistical guarantees in end to end autonomous driving control"
    - "theoretically principled trade off between robustness and accuracy"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "gradient descent finds global minima of deep neural networks"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-3 (induced by source paper "adversarial explanations for understanding image classification decisions and improved neural network robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "certified adversarial robustness via randomized smoothing"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "adversarial attacks and defences competition"
    - "bag of tricks for image classification with convolutional neural networks"
    - "disentangling adversarial robustness and generalization"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "universal approximation with certified networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-5 (induced by source paper "a provable defense for deep residual networks"):
  with members:
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "training verified learners with learned verifiers"
    - "mixtrain scalable training of formally robust neural networks"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"
    - "certified defenses against adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-6 (induced by source paper "adversarial training and robustness for multiple perturbations"):
  with members:
    - "transfer of adversarial robustness between perturbation types"
    - "adversarial perceptual ad blocking meets adversarial machine learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

Connected through hyperedge HE-7 (induced by source paper "adversarial policies attacking deep reinforcement learning"):
  with members:
    - "open ended learning in symmetric zero sum games"
    - "feature denoising for improving adversarial robustness"
    - "deep counterfactual regret minimization"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "are adversarial examples inevitable"
    - "verifiable reinforcement learning via policy extraction"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-8 (induced by source paper "adaptive versus standard descent methods and robustness against adversarial examples"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "cross entropy loss and low rank features have responsibility for adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training with voronoi constraints"
    - "gradient descent finds global minima of deep neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "poincar e glove hyperbolic word embeddings"
    - "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)