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: "teaching uavs to race end to end regression of agile controls in simulation"
  Abstract: Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient on-board processing critical for real-world deployment. From a broader perspective, our results underline the importance of extensive data augmentation techniques to improve robustness in end-to-end learning setups.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"

Connected through hyperedge HE-4 (induced by source paper "learning a controller fusion network by online trajectory filtering for vision based uav racing"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "agile autonomous driving using end to end deep imitation learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cad2rl real single image flight without a single real image"

Connected through hyperedge HE-5 (induced by source paper "sada semantic adversarial diagnostic attacks for autonomous applications"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "towards analyzing semantic robustness of deep neural networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "semantic adversarial examples"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "yolov3 an incremental improvement"
    - "generating 3d adversarial point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"

Connected through hyperedge HE-6 (induced by source paper "oil observational imitation learning"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "deep drone racing learning agile flight in dynamic environments"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "driving policy transfer via modularity and abstraction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "behavioral cloning from observation"

Connected through hyperedge HE-7 (induced by source paper "following high level navigation instructions on a simulated quadcopter with imitation learning"):
  with members:
    - "unifying map and landmark based representations for visual navigation"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "memory augmented control networks"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "probabilistically safe policy transfer"
    - "grounded language learning in a simulated 3d world"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "deep drone racing learning agile flight in dynamic environments"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "agile autonomous driving using end to end deep imitation learning"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "cognitive mapping and planning for visual navigation"
    - "plato policy learning using adaptive trajectory optimization"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "deep neural network for real time autonomous indoor navigation"
    - "orb slam a versatile and accurate monocular slam system"

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.CV(Computer Vision and Pattern Recognition)