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: "robust neural networks using randomized adversarial training"
  Abstract: Since the discovery of adversarial examples in machine learning, researchers have designed several techniques to train neural networks that are robust against different types of attacks (most notably ∞ and 2 based attacks). However , it has been observed that the defense mechanisms designed to protect against one type of attack often offer poor performance against the other. In this paper, we introduce Randomized Adversarial Training (RAT), a technique that is efficient both against 2 and ∞ attacks. To obtain this result, we build upon adversarial training, a technique that is efficient against ∞ attacks, and demonstrate that adding random noise at training and inference time further improves performance against 2 attacks. We then show that RAT is as efficient as adversarial training against ∞ attacks while being robust against strong 2 attacks. Our final comparative experiments demonstrate that RAT outperforms all state-of-the-art approaches against 2 and ∞ attacks.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space"):
  with members:
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "an empirical investigation of randomized defenses against adversarial attacks"
    - "certified adversarial robustness via randomized smoothing"
    - "deep learning for single image super resolution a brief review"
    - "deep learning for multiple image super resolution"
    - "sampling and super resolution of sparse signals beyond the fourier domain"
    - "deep learning for image super resolution a survey"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"

Connected through hyperedge HE-2 (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"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"
    - "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-3 (induced by source paper "towards rapid and robust adversarial training with one step attacks"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "on evaluating adversarial robustness"
    - "theoretical evidence for adversarial robustness through randomization"
    - "towards the first adversarially robust neural network model on mnist"
    - "mixed precision training"
    - "towards robust neural networks via random self ensemble"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-4 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  with members:
    - "discretization based solutions for secure machine learning against adversarial attacks"
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "weight map layer for noise and adversarial attack robustness"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "simple black box adversarial attacks"
    - "gandef a gan based adversarial training defense for neural network classifier"

Connected through hyperedge HE-5 (induced by source paper "structure matters towards generating transferable adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "adversarial training for free"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "semantic adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "exploring adversarial examples in malware detection"

Connected through hyperedge HE-6 (induced by source paper "yet another but more efficient black box adversarial attack tiling and evolution strategies"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "there are no bit parts for sign bits in black box attacks"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"
    - "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-7 (induced by source paper "had gan a human perception auxiliary defense gan to defend adversarial examples"):
  with members:
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "generating adversarial examples with conditional generative adversarial net"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-8 (induced by source paper "improving adversarial robustness via attention and adversarial logit pairing"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "dsfd dual shot face detector"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"

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)