human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning controls using cross modal representations bridging simulation and reality for drone racing"):
  Abstract: Machines are a long way from robustly solving open-world perception-control tasks, such as first-person view (FPV) drone racing. While recent advances in Machine Learning, especially Reinforcement and Imitation Learning show promise, they are constrained by the need of large amounts of difficult to collect real-world data for learning robust behaviors in diverse scenarios. In this work we propose to learn rich representations and policies by leveraging unsupervised data, such as video footage from an FPV drone, together with easy to generate simulated labeled data. Our approach takes a cross-modal perspective, where separate modalities correspond to the raw camera sensor data and the system states relevant to the task, such as the relative pose gates to the UAV. We fuse both data modalities into a novel factored architecture that learns a joint low-dimensional representation via Variational Auto Encoders. Such joint representations allow us to leverage rich labeled information from simulations together with the diversity of possible experiences via the unsupervised real-world data. We present experiments in simulation that provide insights into the rich latent spaces learned with our proposed representations, and also show that the use of our cross-modal architecture improves control policy performance in over 5X in comparison with end-to-end learning or purely unsupervised feature extractors. Finally, we present real-life results for drone navigation, showing that the learned representations and policies can generalize across simulation and reality.

Includes members:
  Member "deep drone racing from simulation to reality with domain randomization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "monet unsupervised scene decomposition and representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "deep drone racing learning agile flight in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "learning latent dynamics for planning from pixels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "world models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

Question: Please tell me which class the source paper 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)