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: "theoretical evidence for adversarial robustness through randomization the case of the exponential family"
  Abstract: This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at inference time. These techniques have proven effective in many contexts, but lack theoretical arguments. We close this gap by presenting a theoretical analysis of these approaches, hence explaining why they perform well in practice. More precisely, we provide the first result relating the randomization rate to robustness to adversarial attacks. This result applies for the general family of exponential distributions, and thus extends and unifies the previous approaches. We support our theoretical claims with a set of experiments.

Connected through hyperedge HE-1 (induced by source paper "learn2perturb an end to end feature perturbation learning to improve adversarial robustness"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robust neural networks using randomized adversarial training"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "feature denoising for improving adversarial robustness"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness through local lipschitzness"):
  with members:
    - "randomized smoothing of all shapes and sizes"
    - "analyzing accuracy loss in randomized smoothing defenses"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "rethinking randomized smoothing for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"

Connected through hyperedge HE-3 (induced by source paper "improving generalization and robustness with noisy collaboration in knowledge distillation"):
  with members:
    - "using videos to evaluate image model robustness"
    - "deep self learning from noisy labels"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"

Connected through hyperedge HE-4 (induced by source paper "yet another but more efficient black box adversarial attack tiling and evolution strategies"):
  with members:
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "there are no bit parts for sign bits in black box attacks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "black box adversarial attacks with limited queries and information"
    - "magnet a two pronged defense against adversarial examples"
    - "neural optimizer search with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "robust neural networks using randomized adversarial training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "a unified view on differential privacy and robustness to adversarial examples"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "on evaluating adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "divide denoise and defend against adversarial attacks"
    - "renyi differential privacy"
    - "mitigating adversarial effects through randomization"
    - "concentrated differential privacy"

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)