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: "adding neural network controllers to behavior trees without destroying performance guarantees"
  Abstract: In this paper, we show how controllers created using data driven designs, such as neural networks, can be used together with model based controllers in a way that combines the performance guarantees of the model based controllers with the efficiency of the data driven controllers. The considered performance guarantees include both safety, in terms of avoiding designated unsafe parts of the state space, and convergence, in terms of reaching a given beneficial part of the state space. Using the framework Behavior Trees, we are able to show how this can be done on the top level, concerning just two controllers, as described above, but also note that the same approach can be used in arbitrary sub-trees. The price for introducing the new controller is that the upper bound on the time needed to reach the desired part of the state space increases. The approach is illustrated with an inverted pendulum example.

Connected through hyperedge HE-1 (induced by source paper "learning a family of optimal state feedback controllers"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "plato policy learning using adaptive trajectory optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "analysis and exploitation of synchronized parallel executions in behavior trees"):
  with members:
    - "hidden markov models derived from behavior trees"
    - "behavior trees in robotics and ai an introduction"
    - "improving the modularity of auv control systems using behaviour trees"
    - "improving the parallel execution of behavior trees"
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-3 (induced by source paper "improving the modularity of auv control systems using behaviour trees"):
  with members:
    - "3d pursuit evasion for auvs"
    - "behavior trees in robotics and ai an introduction"
    - "a two step pursuit evasion algorithm for autonomous underwater vehicles"
    - "towards blended reactive planning and acting using behavior trees"
    - "costar instructing collaborative robots with behavior trees and vision"
    - "learning of behavior trees for autonomous agents"

Connected through hyperedge HE-4 (induced by source paper "towards blended reactive planning and acting using behavior trees"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "improving the modularity of auv control systems using behaviour trees"
    - "learning of behavior trees for autonomous agents"
    - "the ff planning system fast plan generation through heuristic search"

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)