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 training with voronoi constraints"
  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 an adversary could 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 that adversarial training is sample inefficient, and show sufficient sampling conditions under which nearest neighbor classifiers and ball-based adversarial training are robust. Finally we introduce adversarial training with Voronoi constraints, which replaces the norm ball constraint with the Voronoi cell for each point in the training set. We show that adversarial training with Voronoi constraints produces robust models which significantly improve over the state-of-the-art on MNIST and are competitive on CIFAR-10.

Connected through hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-2 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "defending against adversarial examples with k nearest neighbor"
    - "fast rates for a knn classifier robust to unknown asymmetric label noise"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "colored noise injection for training adversarially robust neural networks"):
  with members:
    - "training robust deep neural networks via adversarial noise propagation"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning to defense by learning to attack"
    - "efficient defenses against adversarial attacks"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-4 (induced by source paper "smoothed inference for adversarially trained models"):
  with members:
    - "robust unsupervised neural machine translation with adversarial training"
    - "maxup a simple way to improve generalization of neural network training"
    - "randomized smoothing of all shapes and sizes"
    - "colored noise injection for training adversarially robust neural networks"
    - "enforcing linearity in dnn succours robustness and adversarial image generation"
    - "training robust deep neural networks via adversarial noise propagation"
    - "adversarial policies attacking deep reinforcement learning"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"

Connected through hyperedge HE-5 (induced by source paper "defending against adversarial examples with k nearest neighbor"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "sorting out lipschitz function approximation"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "l2 nonexpansive neural networks"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-6 (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"
    - "on the geometry of adversarial examples"
    - "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)