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: "is deep learning safe for robot vision adversarial examples against the icub humanoid"
  Abstract: Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We then provide a clearer understanding of the safety properties of deep networks through an intuitive empirical analysis, showing that the mapping learned by such networks essentially violates the smoothness assumption of learning algorithms. We finally discuss the main limitations of this work, including the creation of real-world adversarial examples, and sketch promising research directions.

Connected through hyperedge HE-1 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local rule based explanations of black box decision systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "experimental resilience assessment of an open source driving agent"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "software engineering challenges of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "defending against whitebox adversarial attacks via randomized discretization"):
  with members:
    - "adversarial attacks and defences competition"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial logit pairing"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "adversarial learning of deepfakes in accounting"):
  with members:
    - "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"
    - "text based editing of talking head video"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "voice conversion with conditional samplernn"
    - "adversarial text generation via feature mover s distance"
    - "adversarial attacks and defences a survey"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "everybody dance now"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "open dnn box by power side channel attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "finefool fine object contour attack via attention"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "stealing neural networks via timing side channels"
    - "accelerator aware pruning for convolutional neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-6 (induced by source paper "deep neural rejection against adversarial examples"):
  with members:
    - "detecting adversarial examples through nonlinear dimensionality reduction"
    - "selectivenet a deep neural network with an integrated reject option"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "explaining black box android malware detection"
    - "is feature selection secure against training data poisoning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"

Connected through hyperedge HE-7 (induced by source paper "robustness guarantees for deep neural networks on videos"):
  with members:
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "verification of recurrent neural networks through rule extraction"
    - "sparse adversarial perturbations for videos"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "universal adversarial perturbations"
    - "safety verification of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "secml a python library for secure and explainable machine learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial robustness toolbox v1 0 0"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "axiomatic attribution for deep networks"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "why should i trust you explaining the predictions of any classifier"
    - "towards evaluating the robustness 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.LG(Machine Learning)