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: "systematic testing of convolutional neural networks for autonomous driving"
  Abstract: We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles. Our analysis procedure comprises an image generator that produces synthetic pictures by sampling in a lower dimension image modification subspace and a suite of visualization tools. The image generator produces images which can be used to test the CNN and hence expose its vulnerabilities. The presented framework can be used to extract insights of the CNN classifier, compare across classification models, or generate training and validation datasets.

Connected through hyperedge HE-1 (induced by source paper "a formalization of robustness for deep neural networks"):
  with members:
    - "counterexample guided data augmentation"
    - "semantic adversarial deep learning"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "efficient defenses against adversarial attacks"

Connected through hyperedge HE-2 (induced by source paper "comparing offline and online testing of deep neural networks an autonomous car case study"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "paracosm a language and tool for testing autonomous driving systems"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "carla an open urban driving simulator"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "rapidly exploring random trees based test generation for autonomous vehicles"):
  with members:
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "testing deep neural networks"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "a formalization of robustness for deep neural networks"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-5 (induced by source paper "simulation based adversarial test generation for autonomous vehicles with machine learning components"):
  with members:
    - "semantic adversarial deep learning"
    - "deep predictive models for collision risk assessment in autonomous driving"
    - "output range analysis for deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "on a formal model of safe and scalable self driving cars"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-6 (induced by source paper "counterexample guided data augmentation"):
  with members:
    - "semantic adversarial deep learning"
    - "megapixel size image creation using generative adversarial networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "recurrent topic transition gan for visual paragraph generation"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "training region based object detectors with online hard example mining"
    - "a theory of formal synthesis via inductive learning"

Connected through hyperedge HE-7 (induced by source paper "a lidar point cloud generator from a virtual world to autonomous driving"):
  with members:
    - "deep semantic classification for 3d lidar data"
    - "carla an open urban driving simulator"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "detecting drivable area for self driving cars an unsupervised approach"
    - "playing for benchmarks"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-8 (induced by source paper "gray box adversarial testing for control systems with machine learning component"):
  with members:
    - "counterexample guided data augmentation"
    - "reachability analysis and safety verification for neural network control systems"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "output range analysis for deep neural networks"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "towards verified artificial intelligence"
    - "safety verification of deep neural networks"
    - "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.CV(Computer Vision and Pattern Recognition)