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: "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
  Abstract: Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., high-speed and high-acceleration) maneuvers have attracted significant attention in the past few years. In this paper, we propose a novel control law for accurate tracking of aggressive quadcopter trajectories. The proposed method tracks position and yaw angle with their derivatives of up to fourth order, specifically, the position, velocity, acceleration, jerk, and snap along with the yaw angle, yaw rate and yaw acceleration. Two key aspects of the proposed method are the following. First, the controller exploits the differential flatness of the quadcopter dynamics to generate feedforward inputs for attitude rate and attitude acceleration in order to track the jerk and snap references. The tracking is enabled by direct control of body torque using closed-loop control of all four propeller speeds based on optical encoders attached to the motors. Second, the controller utilizes the incremental nonlinear dynamic inversion (INDI) method for accurate tracking of linear and angular accelerations despite external disturbances. Hence, no prior modeling of aerodynamic effects is required. We rigorously analyze the proposed controller through response analysis, and we demonstrate it in experiments. The proposed control law enables a 1-kg quadcopter UAV to track complex 3D trajectories, reaching speeds up to 8.2 m/s and accelerations up to 2g, while keeping the root-mean-square tracking error down to 4 cm, in a flight volume that is roughly 6.5 m long, 6.5 m wide, and 1.5 m tall. We also demonstrate the robustness of the controller by attaching a drag plate to the UAV in flight tests and by pulling on the UAV with a rope during hover.

Connected through hyperedge HE-1 (induced by source paper "aggressive online control of a quadrotor via deep network representations of optimality principles"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "control of a quadrotor with reinforcement learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "trajectory generation for quadrotor based systems using numerical optimal control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-2 (induced by source paper "technical report safe aggressive quadrotor flight via reachability based trajectory design"):
  with members:
    - "guaranteed safe reachability based trajectory design for a high fidelity model of an autonomous passenger vehicle"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"

Connected through hyperedge HE-3 (induced by source paper "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"):
  with members:
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "sim to real learning agile locomotion for quadruped robots"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"

Connected through hyperedge HE-4 (induced by source paper "end to end motion planning of quadrotors using deep reinforcement learning"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "cubeslam monocular 3 d object slam"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "dcad decentralized collision avoidance with dynamics constraints for agile quadrotor swarms"):
  with members:
    - "lswarm efficient collision avoidance for large swarms with coverage constraints in complex urban scenes"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"

Connected through hyperedge HE-6 (induced by source paper "inverting learned dynamics models for aggressive multirotor control"):
  with members:
    - "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-7 (induced by source paper "the blackbird dataset a large scale dataset for uav perception in aggressive flight"):
  with members:
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "torontocity seeing the world with a million eyes"

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)