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: "adef an iterative algorithm to construct adversarial deformations"
  Abstract: While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. #R##N#In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with a convolutional neural network and on ImageNet with Inception-v3 and ResNet-101.

Connected through hyperedge HE-1 (induced by source paper "abba saliency regularized motion based adversarial blur attack"):
  with members:
    - "effects of blur and deblurring to visual object tracking"
    - "brain inspired reverse adversarial examples"
    - "spatial aware online adversarial perturbations against visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "amora black box adversarial morphing attack"
    - "adversarial attacks and defences competition"
    - "learning to synthesize motion blur"

Connected through hyperedge HE-2 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "functional adversarial attacks"
    - "a general framework for adversarial examples with objectives"
    - "panoptic feature pyramid networks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "fine grained synthesis of unrestricted adversarial examples"):
  with members:
    - "neural puppet generative layered cartoon characters"
    - "certified adversarial robustness via randomized smoothing"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "spatially transformed adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-4 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-5 (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"
    - "adversarial vision challenge"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-6 (induced by source paper "amora black box adversarial morphing attack"):
  with members:
    - "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles"
    - "advfaces adversarial face synthesis"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "attacking optical flow"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
    - "machine learning testing survey landscapes and horizons"

Connected through hyperedge HE-7 (induced by source paper "confidence calibrated adversarial training generalizing to unseen attacks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "square attack a query efficient black box adversarial attack via random search"
    - "adversarial robustness against the union of multiple perturbation models"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-8 (induced by source paper "the tale of evil twins adversarial inputs versus backdoored models"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "model reuse attacks on deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "spectral signatures in backdoor attacks"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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.CV(Computer Vision and Pattern Recognition)