human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "synthesis of control barrier functions using a supervised machine learning approach"):
  Abstract: Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing use has assumed full knowledge of the safety barrier functions, however there are cases where the safe regions must be estimated online from sensor measurements. In these cases, the corresponding barrier function must be synthesized online. This paper describes a learning framework for estimating control barrier functions from sensor data. Doing so affords system operation in unknown state space regions without compromising safety. Here, a support vector machine classifier provides the barrier function specification as determined by sets of safe and unsafe states obtained from sensor measurements. Theoretical safety guarantees are provided. Experimental ROS-based simulation results for an omnidirectional robot equipped with LiDAR demonstrate safe operation.

Includes members:
  Member "autonomous navigation in unknown environments using sparse kernel based occupancy mapping":
    also connected through other hyperedges:
      (none)
  Member "control barrier functions theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "an optimal task allocation strategy for heterogeneous multi robot systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive task allocation for heterogeneous multi robot teams with evolving and unknown robot capabilities")
  Member "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration exploitation in constrained mdps")
  Member "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning stable deep dynamics models")
  Member "control of multi agent systems with finite time control barrier certificates and temporal logic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a sequential composition framework for coordinating multi robot behaviors")
  Member "a reduction of imitation learning and structured prediction to no regret online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")

Question: Please tell me which class the source paper 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)