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: "a theoretical framework for robustness of deep classifiers against adversarial examples"
  Abstract: Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifications that lead to incorrect outputs while imperceptible to human eyes. The goal of this paper is not to introduce a single method, but to make theoretical steps towards fully understanding adversarial examples. By using concepts from topology, our theoretical analysis brings forth the key reasons why an adversarial example can fool a classifier ($f_1$) and adds its oracle ($f_2$, like human eyes) in such analysis. By investigating the topological relationship between two (pseudo)metric spaces corresponding to predictor $f_1$ and oracle $f_2$, we develop necessary and sufficient conditions that can determine if $f_1$ is always robust (strong-robust) against adversarial examples according to $f_2$. Interestingly our theorems indicate that just one unnecessary feature can make $f_1$ not strong-robust, and the right feature representation learning is the key to getting a classifier that is both accurate and strong-robust.

Connected through hyperedge HE-1 (induced by source paper "parot a practical framework for robust deep neural network training"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "symbolic execution for deep neural networks"
    - "mixtrain scalable training of formally robust neural networks"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "with friends like these who needs adversaries"

Connected through hyperedge HE-2 (induced by source paper "curls whey boosting black box adversarial attacks"):
  with members:
    - "adversarial vision challenge"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for autonomous defence in software defined networking"):
  with members:
    - "semantic adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-4 (induced by source paper "adversarial attack type i cheat classifiers by significant changes"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "adversarial logit pairing"
    - "adversarial spheres"
    - "a style based generator architecture for generative adversarial networks"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "with friends like these who needs adversaries"):
  with members:
    - "classification regions of deep neural networks"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "mixup beyond empirical risk minimization"
    - "analysis of universal adversarial perturbations"
    - "opening the black box of deep neural networks via information"
    - "measuring the tendency of cnns to learn surface statistical regularities"

Connected through hyperedge HE-6 (induced by source paper "one bit matters understanding adversarial examples as the abuse of redundancy"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial spheres"
    - "a practical approach to sizing neural networks"
    - "the helmholtz method using perceptual compression to reduce machine learning complexity"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-7 (induced by source paper "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "gradient masking causes clever to overestimate adversarial perturbation size"
    - "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"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "towards proving the adversarial robustness of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial attack type i generating false positives"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"
    - "towards proving the adversarial robustness of deep neural networks"
    - "adversarial examples attacks and defenses for deep learning"
    - "practical black box attacks against machine learning"
    - "pyramid scene parsing network"
    - "adversarial examples in the physical world"
    - "deep residual learning for image recognition"

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)