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: "using neural networks to compute approximate and guaranteed feasible hamilton jacobi bellman pde solutions"
  Abstract: To sidestep the curse of dimensionality when computing solutions to Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an algorithm that leverages a neural network to approximate the value function. We show that our final approximation of the value function generates near optimal controls which are guaranteed to successfully drive the system to a target state. Our framework is not dependent on state space discretization, leading to a significant reduction in computation time and space complexity in comparison with dynamic programming-based approaches. Using this grid-free approach also enables us to plan over longer time horizons with relatively little additional computation overhead. Unlike many previous neural network HJB PDE approximating formulations, our approximation is strictly conservative and hence any trajectories we generate will be strictly feasible. For demonstration, we specialize our new general framework to the Dubins car model and discuss how the framework can be applied to other models with higher-dimensional state spaces.

Connected through hyperedge HE-1 (induced by source paper "a classification based approach for approximate reachability"):
  with members:
    - "reverse curriculum generation for reinforcement learning"
    - "neural network with unbounded activation functions is universal approximator"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "learning quadrotor dynamics using neural network for flight control"
    - "plato policy learning using adaptive trajectory optimization"
    - "synthesis of separable controlled invariant sets for modular local control design"

Connected through hyperedge HE-2 (induced by source paper "hamilton jacobi reachability a brief overview and recent advances"):
  with members:
    - "recursive regression with neural networks approximating the hji pde solution"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"
    - "funnel libraries for real time robust feedback motion planning"
    - "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming"
    - "safe sequential path planning of multi vehicle systems under presence of disturbances and imperfect information"

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.LG(Machine Learning)