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: "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
  Abstract: We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation in the world reference frame, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments, achieving a success rate of 80% and gap orientations up to 45°. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap.

Connected through hyperedge HE-1 (induced by source paper "deep drone racing from simulation to reality with domain randomization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep drone racing learning agile flight in dynamic environments"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"

Connected through hyperedge HE-2 (induced by source paper "a 64 mw dnn based visual navigation engine for autonomous nano drones"):
  with members:
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "learning to fly by crashing"
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
    - "cad2rl real single image flight without a single real image"
    - "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "closing the accuracy gap in an event based visual recognition task"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "fast and efficient asynchronous neural computation with adapting spiking neural networks"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-4 (induced by source paper "flying through a narrow gap using neural network an end to end planning and control approach"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "deep drone racing learning agile flight in dynamic environments"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "visual model predictive localization for computationally efficient autonomous racing of a 72 gram drone"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"

Connected through hyperedge HE-6 (induced by source paper "joint vision based navigation control and obstacle avoidance for uavs in dynamic environments"):
  with members:
    - "effective target aware visual navigation for uavs"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"

Connected through hyperedge HE-7 (induced by source paper "nailed it autonomous roofing with a nailgun equipped octocopter"):
  with members:
    - "experimental evaluation of continuum deformation with a five quadrotor team"

Connected through hyperedge HE-8 (induced by source paper "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"):
  with members:
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "interactive perception leveraging action in perception and perception in action"
    - "fast optical flow using dense inverse search"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "unsupervised monocular depth estimation with left right consistency"
    - "direct sparse odometry"

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)