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: "dryvr data driven verification and compositional reasoning for automotive systems"
  Abstract: We present the DRYVR framework for verifying hybrid control systems that are described by a combination of a black-box simulator for trajectories and a white-box transition graph specifying mode switches. The framework includes (a) a probabilistic algorithm for learning sensitivity of the continuous trajectories from simulation data, (b) a bounded reachability analysis algorithm that uses the learned sensitivity, and (c) reasoning techniques based on simulation relations and sequential composition, that enable verification of complex systems under long switching sequences, from the reachability analysis of a simpler system under shorter sequences. We demonstrate the utility of the framework by verifying a suite of automotive benchmarks that include powertrain control, automatic transmission, and several autonomous and ADAS features like automatic emergency braking, lane-merge, and auto-passing controllers.

Connected through hyperedge HE-1 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"

Connected through hyperedge HE-2 (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"
    - "towards practical verification of machine learning the case of computer vision systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "online monitoring for safe pedestrian vehicle interactions"):
  with members:
    - "numerical verification of affine systems with up to a billion dimensions"
    - "yolov3 an incremental improvement"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "evaluation of automated vehicles encountering pedestrians at unsignalized crossings"

Connected through hyperedge HE-4 (induced by source paper "approximation refinement testing of compute intensive cyber physical models an approach based on system identification"):
  with members:
    - "evaluating model testing and model checking for finding requirements violations in simulink models"
    - "generating automated and online test oracles for simulink models with continuous and uncertain behaviors"
    - "falsification of cyber physical systems using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "multi agent safety verification using symmetry transformations"):
  with members:
    - "numerical verification of affine systems with up to a billion dimensions"
    - "bounded verification with on the fly discrepancy computation"

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.SY(Systems and Control)