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: "nonlinear model predictive guidance for fixed wing uavs using identified control augmented dynamics"
  Abstract: In this paper, we address the modeling and identification of control augmented dynamics for a small fixed-wing Unmanned Aerial Vehicle (UAV) with a widely available off-the-shelf (OTS) autopilot in the loop, utilizing a standard sensor suite. A high-level Nonlinear Model Predictive Controller (NMPC) is subsequently formulated for simultaneous airspeed stabilization, path following, and soft constraint handling, using the identified model for horizon propagation. The approach is explored in several exemplary flight experiments including path following of helix and connected Dubins Aircraft segments in high winds as well as a motor failure scenario. The cost function, insights on its weighting, and additional soft constraints used throughout the experimentation are discussed.

Connected through hyperedge HE-1 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "object finding in cluttered scenes using interactive perception"
    - "deep value model predictive control"
    - "whole body mpc for a dynamically stable mobile manipulator"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a full controller for a fixed wing uav"):
  with members:
    - "a simple controller for the transition maneuver of a tail sitter drone"

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)