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: "deep drone racing from simulation to reality with domain randomization"
  Abstract: Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network. The resulting modular system is both platform independent and domain independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate  zero-shot sim-to-real  transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.

Connected through hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "neuroflight next generation flight control firmware"
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "real time reinforcement learning"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "continuous control with contexts provably"
    - "learning agile and dynamic motor skills for legged robots"
    - "h_ infty model free reinforcement learning with robust stability guarantee"
    - "robust model free reinforcement learning with multi objective bayesian optimization"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "sim to real learning agile locomotion for quadruped robots"

Connected through hyperedge HE-3 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "state representation learning for control an overview"

Connected through hyperedge HE-4 (induced by source paper "airsim drone racing lab"):
  with members:
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "event based vision a survey"
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-5 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"
    - "deepmind control suite"

Connected through hyperedge HE-6 (induced by source paper "learning controls using cross modal representations bridging simulation and reality for drone racing"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "state representation learning for control an overview"

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)