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 vision challenge"
  Abstract: This competition was meant to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. It encouraged researchers to develop query-efficient adversarial attacks that can successfully operate against a wide range of defenses while just observing the final model decision to generate adversarial examples. Conversely, the competition encouraged the development of new defenses that can resist a wide range of strong decision-based attacks. In this chapter we describe the organisation and structure of the challenge as well as the solutions developed by the top-ranking teams.

Connected through hyperedge HE-1 (induced by source paper "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "design and interpretation of universal adversarial patches in face detection"
    - "theoretically principled trade off between robustness and accuracy"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-2 (induced by source paper "theoretically principled trade off between robustness and accuracy"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-3 (induced by source paper "towards large yet imperceptible adversarial image perturbations with perceptual color distance"):
  with members:
    - "perceptual quality preserving black box attack against deep learning image classifiers"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "smooth adversarial examples"
    - "a general framework for adversarial examples with objectives"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "towards imperceptible and robust adversarial example attacks against neural networks"

Connected through hyperedge HE-4 (induced by source paper "black box adversarial attack with transferable model based embedding"):
  with members:
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "a direct approach to robust deep learning using adversarial networks"
    - "simple black box adversarial attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (induced by source paper "curls whey boosting black box adversarial attacks"):
  with members:
    - "improving transferability of adversarial examples with input diversity"
    - "learning transferable architectures for scalable image recognition"
    - "magnet a two pronged defense against adversarial examples"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "squeeze and excitation networks"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-6 (induced by source paper "rallying adversarial techniques against deep learning for network security"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "adversarial attacks and defences competition"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "adversarial spheres"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "hu fu hardware and software collaborative attack framework against neural networks"

Connected through hyperedge HE-7 (induced by source paper "benchmarking adversarial robustness"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "on evaluating adversarial robustness"
    - "a critique of the deepsec platform for security analysis of deep learning models"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "adversarial attacks and defences competition"
    - "adversarial logit pairing"

Connected through hyperedge HE-8 (induced by source paper "on norm agnostic robustness of adversarial training"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "improving the generalization of adversarial training with domain adaptation"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

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)