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: "soter programming safe robotics system using runtime assurance"
  Abstract: Autonomous robots increasingly depend on third-party off-the-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-time certification of correct operation. To address this challenge, we present SOTER, a programming framework that integrates the core principles of runtime assurance to enable the use of uncertified controllers, while still providing safety guarantees. #R##N#Runtime Assurance (RTA) is an approach used for safety-critical systems where design-time analysis is coupled with run-time techniques to switch between unverified advanced controllers and verified simple controllers. In this paper, we present a runtime assurance programming framework for modular design of provably-safe robotics software. \tool provides language primitives to declaratively construct a \rta module consisting of an advanced controller (untrusted), a safe controller (trusted), and the desired safety specification (S). If the RTA module is well formed then the framework provides a formal guarantee that it satisfies property S. The compiler generates code for monitoring system state and switching control between the advanced and safe controller in order to guarantee S. RTA allows complex systems to be constructed through the composition of RTA modules. #R##N#To demonstrate the efficacy of our framework, we consider a real-world case-study of building a safe drone surveillance system. Our experiments both in simulation and on actual drones show that RTA-enabled RTA ensures safety of the system, including when untrusted third-party components have bugs or deviate from the desired behavior.

Connected through hyperedge HE-1 (induced by source paper "input validation for neural networks via runtime local robustness verification"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "outside the box abstraction based monitoring of neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "a new simulation metric to determine safe environments and controllers for systems with unknown dynamics"):
  with members:
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "goal driven dynamics learning via bayesian optimization"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "learning quadrotor dynamics using neural network for flight control"
    - "diagnosis and repair for synthesis from signal temporal logic specifications"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-3 (induced by source paper "soter a runtime assurance framework for programming safe robotics systems"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "a component based simplex architecture for high assurance cyber physical systems"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "verified runtime validation for partially observable hybrid systems"):
  with members:
    - "differential equation axiomatization the impressive power of differential ghosts"
    - "a complete uniform substitution calculus for differential dynamic logic"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "formal verification of obstacle avoidance and navigation of ground robots"
    - "robust online monitoring of signal temporal logic"
    - "on line monitoring for temporal logic robustness"
    - "counter strategy guided refinement of gr 1 temporal logic specifications"

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.RO(Robotics)