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: "towards practical verification of machine learning the case of computer vision systems"
  Abstract: Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. In this paper, we propose a generic framework for evaluating security and robustness of ML systems using different real-world safety properties. We further design, implement and evaluate VeriVis, a scalable methodology that can verify a diverse set of safety properties for state-of-the-art computer vision systems with only blackbox access. VeriVis leverage different input space reduction techniques for efficient verification of different safety properties. VeriVis is able to find thousands of safety violations in fifteen state-of-the-art computer vision systems including ten Deep Neural Networks (DNNs) such as Inception-v3 and Nvidia's Dave self-driving system with thousands of neurons as well as five commercial third-party vision APIs including Google vision and Clarifai for twelve different safety properties. Furthermore, VeriVis can successfully verify local safety properties, on average, for around 31.7% of the test images. VeriVis finds up to 64.8x more violations than existing gradient-based methods that, unlike VeriVis, cannot ensure non-existence of any violations. Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60.2%.

Connected through hyperedge HE-1 (induced by source paper "certification of semantic perturbations via randomized smoothing"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient formal safety analysis of neural networks"

Connected through hyperedge HE-2 (induced by source paper "testing dnn image classifier for confusion bias errors"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "metric learning for adversarial robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "noise tolerant fair classification"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "visual interpretability for deep learning a survey"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "benchmarking robustness in object detection autonomous driving when winter is coming"):
  with members:
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "rain removal in traffic surveillance does it matter"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fixing the train test resolution discrepancy"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-4 (induced by source paper "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"):
  with members:
    - "equivariant transformer networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "unsupervised data augmentation for consistency training"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generalisation in humans and deep neural networks"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"

Connected through hyperedge HE-5 (induced by source paper "identifying model weakness with adversarial examiner"):
  with members:
    - "using videos to evaluate image model robustness"
    - "embodied visual recognition"
    - "meta sim learning to generate synthetic datasets"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "realistic adversarial examples in 3d meshes"
    - "deep nets what have they ever done for vision"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"

Connected through hyperedge HE-6 (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"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "robust physical world attacks on machine learning models"

Connected through hyperedge HE-7 (induced by source paper "mnist c a robustness benchmark for computer vision"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "a direct approach to robust deep learning using adversarial networks"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "on evaluating adversarial robustness"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

Connected through hyperedge HE-8 (induced by source paper "ml based fault injection for autonomous vehicles a case for bayesian fault injection"):
  with members:
    - "kayotee a fault injection based system to assess the safety and reliability of autonomous vehicles to faults and errors"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "experimental resilience assessment of an open source driving agent"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "carla an open urban driving simulator"
    - "dryvr data driven verification and compositional reasoning for automotive systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "you only look once unified real time object detection"

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.CR(Cryptography and Security)