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: "sampling based temporal logic path planning"
  Abstract: In this paper, we propose a sampling-based motion planning algorithm that finds an infinite path satisfying a Linear Temporal Logic (LTL) formula over a set of properties satisfied by some regions in a given environment. The algorithm has three main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which guarantees the low complexity of the overall method. Third, it is probabilistically complete. Examples illustrating the usefulness and the performance of the method are included.

Connected through hyperedge HE-1 (induced by source paper "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  with members:
    - "logically constrained reinforcement learning"
    - "omega regular objectives in model free reinforcement learning"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "correct by synthesis reinforcement learning with temporal logic constraints"

Connected through hyperedge HE-2 (induced by source paper "an abstraction free method for multi robot temporal logic optimal control synthesis"):
  with members:
    - "stylus a temporal logic optimal control synthesis algorithm for large scale multi robot systems"
    - "control of magnetic microrobot teams for temporal micromanipulation tasks"
    - "learning sampling distributions for robot motion planning"
    - "decentralized abstractions for multi agent systems under coupled constraints"
    - "optimal multi robot path planning with ltl constraints guaranteeing correctness through synchronization"
    - "sampling based algorithms for optimal motion planning"
    - "synthesis of distributed control and communication schemes from global ltl specifications"
    - "linear encodings of bounded ltl model checking"

Connected through hyperedge HE-3 (induced by source paper "stylus a temporal logic optimal control synthesis algorithm for large scale multi robot systems"):
  with members:
    - "probabilistically safe corridors to guide sampling based motion planning"
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "t a heuristic search based algorithm for motion planning with temporal goals"
    - "control of magnetic microrobot teams for temporal micromanipulation tasks"
    - "learning sampling distributions for robot motion planning"
    - "decentralized abstractions for multi agent systems under coupled constraints"
    - "optimal multi robot path planning with ltl constraints guaranteeing correctness through synchronization"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "t a heuristic search based algorithm for motion planning with temporal goals"):
  with members:
    - "optimal path planning under temporal logic constraints"

Connected through hyperedge HE-5 (induced by source paper "sampling based optimal control synthesis for multi robot systems under global temporal tasks"):
  with members:
    - "decentralized abstractions for multi agent systems under coupled constraints"
    - "sampling based algorithms for optimal motion planning"
    - "synthesis of distributed control and communication schemes from global ltl 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)