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 examples a complete characterisation of the phenomenon"
  Abstract: We provide a complete characterisation of the phenomenon of adversarial examples - inputs intentionally crafted to fool machine learning models. We aim to cover all the important concerns in this field of study: (1) the conjectures on the existence of adversarial examples, (2) the security, safety and robustness implications, (3) the methods used to generate and (4) protect against adversarial examples and (5) the ability of adversarial examples to transfer between different machine learning models. We provide ample background information in an effort to make this document self-contained. Therefore, this document can be used as survey, tutorial or as a catalog of attacks and defences using adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "impact of low bitwidth quantization on the adversarial robustness for embedded neural networks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "on evaluating adversarial robustness"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "stochastic activation pruning for robust adversarial defense"
    - "efficient two step adversarial defense for deep neural networks"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-2 (induced by source paper "active subspace of neural networks structural analysis and universal attacks"):
  with members:
    - "universal adversarial audio perturbations"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "bayesian tensorized neural networks with automatic rank selection"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "gradient descent based d optimal design for the least squares polynomial approximation"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "the attack generator a systematic approach towards constructing adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "spatially transformed adversarial examples"
    - "playing the game of universal adversarial perturbations"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "characterizing the shape of activation space in deep neural networks"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "detecting adversarial perturbations through spatial behavior in activation spaces"
    - "improving the generalization of adversarial training with domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "neural network verification for the masses of ai graduates"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "detecting adversarial examples in convolutional neural networks"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "safety verification of deep neural networks"
    - "intriguing properties of 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.CV(Computer Vision and Pattern Recognition)