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: "exploring the space of adversarial images"
  Abstract: Adversarial examples have raised questions regarding the robustness and security of deep neural networks. In this work we formalize the problem of adversarial images given a pretrained classifier, showing that even in the linear case the resulting optimization problem is nonconvex. We generate adversarial images using shallow and deep classifiers on the MNIST and ImageNet datasets. We probe the pixel space of adversarial images using noise of varying intensity and distribution. We bring novel visualizations that showcase the phenomenon and its high variability. We show that adversarial images appear in large regions in the pixel space, but that, for the same task, a shallow classifier seems more robust to adversarial images than a deep convolutional network.

Connected through hyperedge HE-1 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "heat and blur an effective and fast defense against adversarial examples"):
  with members:
    - "concise explanations of neural networks using adversarial training"
    - "adversarial examples are a natural consequence of test error in noise"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "saliency methods for explaining adversarial attacks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "spatially transformed adversarial examples"
    - "interpretable deep learning under fire"
    - "concise and stable explanations using adversarial training"

Connected through hyperedge HE-3 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-4 (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-5 (induced by source paper "random directional attack for fooling deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "semantic adversarial examples"
    - "disentangling adversarial robustness and generalization"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "comdefend an efficient image compression model to defend adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "perfectly parallel fairness certification of neural networks"):
  with members:
    - "probabilistic verification of fairness properties via concentration"
    - "a provable defense for deep residual networks"
    - "a survey on bias and fairness in machine learning"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "the efficacy of shield under different threat models"
    - "deepsearch simple and effective blackbox fuzzing of deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "mixup inference better exploiting mixup to defend adversarial attacks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"
    - "data interpolating prediction alternative interpretation of mixup"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial attacks and defences competition"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-8 (induced by source paper "optimization and abstraction a synergistic approach for analyzing neural network robustness"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "an overview of vulnerabilities of voice controlled systems"
    - "robust physical world attacks on deep learning models"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "output range analysis for deep neural networks"
    - "towards practical verification of machine learning the case of computer vision systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepsafe a data driven approach for checking adversarial robustness in 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.NE(Neural and Evolutionary Computing)