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: "planning fast and slow a framework for adaptive real time safe trajectory planning"
  Abstract: Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost. This work builds on a recent development called FaSTrack in which a slow offline computation provides a modular safety guarantee for a faster online planner. We introduce the notion of "meta-planning" in which a refined offline computation enables safe switching between different online planners. This provides autonomous systems with the ability to adapt motion plans to a priori unknown environments in real-time as sensor measurements detect new obstacles, and the flexibility to maneuver differently in the presence of obstacles than they would in free space, all while maintaining a strict safety guarantee. We demonstrate the meta-planning algorithm both in simulation and in hardware using a small Crazyflie 2.0 quadrotor.

Connected through hyperedge HE-1 (induced by source paper "verifiably safe off model reinforcement learning"):
  with members:
    - "logically correct reinforcement learning"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "shielded decision making in mdps"
    - "falsification of cyber physical systems using deep reinforcement learning"
    - "a complete uniform substitution calculus for differential dynamic logic"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-2 (induced by source paper "real time funnel generation for restricted motion planning"):
  with members:
    - "robust tracking with model mismatch for fast and safe planning an sos optimization approach"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "funnel libraries for real time robust feedback motion planning"
    - "control contraction metrics convex and intrinsic criteria for nonlinear feedback design"

Connected through hyperedge HE-3 (induced by source paper "safe and fast tracking control on a robot manipulator robust mpc and neural network control"):
  with members:
    - "large scale model predictive control with neural networks and primal active sets"
    - "learning an approximate model predictive controller with guarantees"
    - "implementation of nonlinear model predictive path following control for an industrial robot"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-4 (induced by source paper "where do you think you re going inferring beliefs about dynamics from behavior"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "impossibility of deducing preferences and rationality from human policy"
    - "reinforcement learning with deep energy based policies"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-5 (induced by source paper "a scalable framework for real time multi robot multi human collision avoidance"):
  with members:
    - "classification based approximate reachability with guarantees applied to safe trajectory tracking"
    - "a classification based approach for approximate reachability"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "robust tracking with model mismatch for fast and safe planning an sos optimization approach"
    - "cooperative multi agent planning a survey"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-6 (induced by source paper "safely probabilistically complete real time planning and exploration in unknown environments"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "funnel libraries for real time robust feedback motion planning"
    - "learning model predictive control for iterative tasks a data driven control framework"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-7 (induced by source paper "probabilistically safe robot planning with confidence based human predictions"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "guided cost learning deep inverse optimal control via policy optimization"

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)