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: "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.

Connected through hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty"):
  with members:
    - "control barrier functions theory and applications"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "algorithms for verifying deep neural networks"
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "worst cases policy gradients"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"

Connected through hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates"):
  with members:
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "algorithms for verifying deep neural networks"
    - "worst cases policy gradients"
    - "oil observational imitation learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "reluplex an efficient smt solver for verifying deep neural networks"

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)