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 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

Includes members:
  Member "deep drone racing learning agile flight in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "a real time game theoretic planner for autonomous two player drone racing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "airsim drone racing lab")
  Member "following high level navigation instructions on a simulated quadcopter with imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "affect based intrinsic rewards for exploration and learning")
  Member "sim4cv a photo realistic simulator for computer vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to train with synthetic humans")
  Member "teaching uavs to race end to end regression of agile controls in simulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc")
  Member "agile autonomous driving using end to end deep imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.RO(Robotics)