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: "real time control for autonomous racing based on viability theory"
  Abstract: In this paper we consider autonomous driving of miniature race cars. The viability kernel is used to efficiently generate finite look ahead trajectories that maximize progress while remaining recursively feasible with respect to static obstacles (e.g., stay inside the track). Together with a low level model predictive controller, this method makes real time autonomous racing possible. The viability kernel computation is based on space discretization. To make the calculation robust against discretization errors, we propose a novel numerical scheme based on game theoretical methods, in particular the discriminating kernel. We show that the resulting algorithm provides an inner approximation of the viability kernel and guarantees that, for all states in the cell surrounding a viable grid point, there exists a control that keeps the system within the kernel. The performance of the proposed control method is studied in simulation where we determine the effects of various design choices and parameters and in experiments on an autonomous racing set-up maintained at the Automatic Control Laboratory of ETH Zurich. Both simulation and experimental results suggest that the more conservative approximation using the discriminating kernel results in safer driving (in terms of constraint violations) at the cost of a small increase in lap time.

Connected through hyperedge HE-1 (induced by source paper "optimization based hierarchical motion planning for autonomous racing"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-2 (induced by source paper "amz driverless the full autonomous racing system"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "autorally an open platform for aggressive autonomous driving"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-3 (induced by source paper "a learnable safety measure"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "feedback control for cassie with deep reinforcement learning"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "sample based learning model predictive control for linear uncertain systems"):
  with members:
    - "learning how to autonomously race a car a predictive control approach"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "linear model predictive safety certification for learning based control"
    - "learning based predictive control for linear systems a unitary approach"
    - "learning based model predictive control for safe exploration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-5 (induced by source paper "learning a curve guardian for motorcycles"):
  with members:
    - "learning accurate comfortable and human like driving"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "deep residual learning for image recognition"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-6 (induced by source paper "learning how to autonomously race a car a predictive control approach"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "autonomous racing using learning model predictive control"
    - "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control"

Connected through hyperedge HE-7 (induced by source paper "beyond basins of attraction evaluating robustness of natural dynamics"):
  with members:
    - "walking with confidence safety regulation for full order biped models"
    - "gait learning for soft microrobots controlled by light fields"
    - "learning from outside the viability kernel why we should build robots that can fall with grace"
    - "robust optimal planning and control of non periodic bipedal locomotion with a centroidal momentum model"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "shaping in practice training wheels to learn fast hopping directly in hardware"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"

Connected through hyperedge HE-8 (induced by source paper "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "funnel libraries for real time robust feedback motion planning"
    - "ssd single shot multibox detector"

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)