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: "unscented external force and torque estimation for quadrotors"
  Abstract: In this paper, we describe an algorithm, based on the well-known Unscented Quaternion Estimator, to estimate external forces and torques acting on a quadrotor. This formulation uses a non-linear model for the quadrotor dynamics, naturally incorporates process and measurement noise, requires only a few parameters to be tuned manually, and uses singularity-free unit quaternions to represent attitude. We demonstrate in simulation that the proposed algorithm can outperform existing methods. We then highlight how our approach can be used to generate force and torque profiles from experimental data, and how this information can later be used for controller design. Finally, we show how the resulting controllers enable a quadrotor to stay in the wind field of a moving fan.

Connected through hyperedge HE-1 (induced by source paper "touch the wind simultaneous airflow drag and interaction sensing on a multirotor"):
  with members:
    - "contact inertial odometry collisions are your friend"
    - "neural lander stable drone landing control using learned dynamics"
    - "robust collaborative object transportation using multiple mavs"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "disturbance estimation and rejection for high precision multirotor position control"):
  with members:
    - "long duration autonomy for small rotorcraft uas including recharging"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"

Connected through hyperedge HE-3 (induced by source paper "robust collaborative object transportation using multiple mavs"):
  with members:
    - "a decentralized multi agent unmanned aerial system to search pick up and relocate objects"
    - "aerial picking and delivery of magnetic objects with mavs"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"

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)