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 "oil observational imitation learning"):
  Abstract: Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images. However, these approaches tend to be sensitive to mistakes by the teacher and do not scale well to other environments or vehicles. To this end, we propose Observational Imitation Learning (OIL), a novel imitation learning variant that supports online training and automatic selection of optimal behavior by observing multiple imperfect teachers. We apply our proposed methodology to the challenging problems of autonomous driving and UAV racing. For both tasks, we utilize the Sim4CV simulator that enables the generation of large amounts of synthetic training data and also allows for online learning and evaluation. We train a perception network to predict waypoints from raw image data and use OIL to train another network to predict controls from these waypoints. Extensive experiments demonstrate that our trained network outperforms its teachers, conventional imitation learning (IL) and reinforcement learning (RL) baselines and even humans in simulation. The project website is available at this https URL and a video at this https URL

Includes members:
  Member "learning a controller fusion network by online trajectory filtering for vision based uav racing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization")
  Member "deep drone racing learning agile flight in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "cirl controllable imitative reinforcement learning for vision based self driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal end to end autonomous driving")
  Member "conditional affordance learning for driving in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "driving policy transfer via modularity and abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to move with affordance maps")
  Member "following high level navigation instructions on a simulated quadcopter with imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "learning to train with synthetic humans")
  Member "behavioral cloning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")

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)