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: "a component based simplex architecture for high assurance cyber physical systems"
  Abstract: We present Component-Based Simplex Architecture (CBSA), a new framework for assuring the runtime safety of component-based cyber-physical systems (CPSs). CBSA integrates Assume-Guarantee (A-G) reasoning with the core principles of the Simplex control architecture to allow component-based CPSs to run advanced, uncertified controllers while still providing runtime assurance that A-G contracts and global properties are satisfied. In CBSA, multiple Simplex instances, which can be composed in a nested, serial or parallel manner, coordinate to assure system-wide properties. Combining A-G reasoning and the Simplex architecture is a challenging problem that yields significant benefits. By utilizing A-G contracts, we are able to compositionally determine the switching logic for CBSAs, thereby alleviating the state explosion encountered by other approaches. Another benefit is that we can use A-G proof rules to decompose the proof of system-wide safety assurance into sub-proofs corresponding to the component-based structure of the system architecture. We also introduce the notion of coordinated switching between Simplex instances, a key component of our compositional approach to reasoning about CBSA switching logic. We illustrate our framework with a component-based control system for a ground rover. We formally prove that the CBSA for this system guarantees energy safety (the rover never runs out of power), and collision freedom (the rover never collides with a stationary obstacle). We also consider a CBSA for the rover that guarantees mission completion: all target destinations visited within a prescribed amount of time.

Connected through hyperedge HE-1 (induced by source paper "dynamic weighted simplex strategy for learning enabled cyber physical systems"):
  with members:
    - "attacking vision based perception in end to end autonomous driving models"
    - "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "augmenting learning components for safety in resource constrained autonomous robots"):
  with members:
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "limits of end to end learning"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "end to end learning for self driving cars"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "soter a runtime assurance framework for programming safe robotics systems"):
  with members:
    - "soter programming safe robotics system using runtime assurance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "towards verified artificial intelligence"
    - "robust online monitoring of signal temporal logic"
    - "a verifiable and correct by construction controller for robot functional levels"
    - "safe exploration in markov decision processes"
    - "sampling based algorithms for optimal motion planning"

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)