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 a controller fusion network by online trajectory filtering for vision based uav racing"
  Abstract: Autonomous UAV racing has recently emerged as an interesting research problem. The dream is to beat humans in this new fast-paced sport. A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert. However, such a policy is limited by the expert it imitates and scaling to other environments and vehicle dynamics is difficult. One approach to overcome the drawbacks of an end-to-end policy is to train a network only on the perception task and handle control with a PID or MPC controller. However, a single controller must be extensively tuned and cannot usually cover the whole state space. In this paper, we propose learning an optimized controller using a DNN that fuses multiple controllers. The network learns a robust controller with online trajectory filtering, which suppresses noisy trajectories and imperfections of individual controllers. The result is a network that is able to learn a good fusion of filtered trajectories from different controllers leading to significant improvements in overall performance. We compare our trained network to controllers it has learned from, end-to-end baselines and human pilots in a realistic simulation; our network beats all baselines in extensive experiments and approaches the performance of a professional human pilot. A video summarizing this work is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "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"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-2 (induced by source paper "oil observational imitation learning"):
  with members:
    - "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"
    - "teaching uavs to race end to end regression of agile controls in simulation"

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)