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: "humans can decipher adversarial images"
  Abstract: Does the human mind resemble the machine-learning systems that mirror its performance? Convolutional neural networks (CNNs) have achieved human-level benchmarks in classifying novel images. These advances support technologies such as autonomous vehicles and machine diagnosis; but beyond this, they serve as candidate models for human vision itself. However, unlike humans, CNNs are “fooled” by adversarial examples—nonsense patterns that machines recognize as familiar objects, or seemingly irrelevant image perturbations that nevertheless alter the machine’s classification. Such bizarre behaviors challenge the promise of these new advances; but do human and machine judgments fundamentally diverge? Here, we show that human and machine classification of adversarial images are robustly related: In 8 experiments on 5 prominent and diverse adversarial imagesets, human subjects correctly anticipated the machine’s preferred label over relevant foils—even for images described as “totally unrecognizable to human eyes”. Human intuition may be a surprisingly reliable guide to machine (mis)classification—with consequences for minds and machines alike.

Connected through hyperedge HE-1 (induced by source paper "a hierarchy of limitations in machine learning"):
  with members:
    - "case study predictive fairness to reduce misdemeanor recidivism through social service interventions"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "the human body is a black box supporting clinical decision making with deep learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "an open source automl benchmark"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "roles for computing in social change"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"

Connected through hyperedge HE-2 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"

Connected through hyperedge HE-3 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"

Connected through hyperedge HE-4 (induced by source paper "upc learning universal physical camouflage attacks on object detectors"):
  with members:
    - "robust adversarial perturbation on deep proposal based models"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "textbugger generating adversarial text against real world applications"
    - "yolov3 an incremental improvement"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "dpatch an adversarial patch attack on object detectors"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-5 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "conditional generative models are not robust"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "deep predictive coding network for object recognition"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

Connected through hyperedge HE-6 (induced by source paper "improving the robustness of imagenet classifiers using elements of human visual cognition"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "interpreting adversarially trained convolutional neural networks"
    - "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"
    - "a simple cache model for image recognition"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "generalisation in humans and deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-7 (induced by source paper "a surprising density of illusionable natural speech"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial attacks on neural network policies"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "robust physical world attacks on machine learning models"
    - "it takes two to tango towards theory of ai s mind"

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)