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: "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
  Abstract: Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poorly or not at all, and the systematic differences between simulation and the real world are typically impossible to eliminate. In this work, we investigate how data from both simulation and the real world can be combined in a hybrid deep reinforcement learning algorithm. Our method uses real-world data to learn about the dynamics of the system, and simulated data to learn a generalizable perception system that can enable the robot to avoid collisions using only a monocular camera. We demonstrate our approach on a real-world nano aerial vehicle collision avoidance task, showing that with only an hour of real-world data, the quadrotor can avoid collisions in new environments with various lighting conditions and geometry. Code, instructions for building the aerial vehicles, and videos of the experiments can be found at github.com/gkahn13/GtS

Connected through hyperedge HE-1 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "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-2 (induced by source paper "robust visual domain randomization for reinforcement learning"):
  with members:
    - "active domain randomization"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "generalization and regularization in dqn"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "low level control of a quadrotor with deep model based reinforcement learning"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "fly safe aerial swarm robotics using force field particle swarm optimisation"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"

Connected through hyperedge HE-4 (induced by source paper "simulation based reinforcement learning for real world autonomous driving"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "model based reinforcement learning for atari"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the limits and potentials of deep learning for robotics"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-5 (induced by source paper "situational fusion of visual representation for visual navigation"):
  with members:
    - "neural autonomous navigation with riemannian motion policy"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "virtual to real learning to control in visual semantic segmentation"
    - "driving policy transfer via modularity and abstraction"
    - "learning to navigate in cities without a map"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-6 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-7 (induced by source paper "interactive gibson benchmark for interactive navigation in cluttered environments"):
  with members:
    - "doorgym a scalable door opening environment and baseline agent"
    - "vrkitchen an interactive 3d virtual environment for task oriented learning"
    - "deep visual mpc policy learning for navigation"
    - "habitat a platform for embodied ai research"
    - "gibson env real world perception for embodied agents"
    - "unity a general platform for intelligent agents"
    - "on evaluation of embodied navigation agents"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-8 (induced by source paper "learning controls using cross modal representations bridging simulation and reality for drone racing"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "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.LG(Machine Learning)