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 by cheating"
  Abstract: Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by decomposing it into two stages. We first train an agent that has access to privileged information. This privileged agent cheats by observing the ground-truth layout of the environment and the positions of all traffic participants. In the second stage, the privileged agent acts as a teacher that trains a purely vision-based sensorimotor agent. The resulting sensorimotor agent does not have access to any privileged information and does not cheat. This two-stage training procedure is counter-intuitive at first, but has a number of important advantages that we analyze and empirically demonstrate. We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state of the art on the CARLA benchmark and the recent NoCrash benchmark. Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state of the art. For the video that summarizes this work, see this https URL

Connected through hyperedge HE-1 (induced by source paper "end to end model free reinforcement learning for urban driving using implicit affordances"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "is deep reinforcement learning really superhuman on atari leveling the playing field"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "end to end race driving with deep reinforcement learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "end to end vehicle lateral control using a single fisheye camera"

Connected through hyperedge HE-2 (induced by source paper "lates latent space distillation for teacher student driving policy learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "virtual to real reinforcement learning for autonomous driving"
    - "carla an open urban driving simulator"

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)