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 "lates latent space distillation for teacher student driving policy learning"):
  Abstract: We describe a policy learning approach to map visual inputs to driving controls that leverages side information on semantics and affordances of objects in the scene from a secondary teacher model. While the teacher receives semantic segmentation and stop "intention" values as inputs and produces an estimate of the driving controls, the primary student model only receives images as inputs, and attempts to imitate the controls while being biased towards the latent representation of the teacher model. The latent representation encodes task-relevant information in the inputs of the teacher model, which are semantic segmentation of the image, and intention values for driving controls in the presence of objects in the scene such as vehicles, pedestrians and traffic lights. Our student model does not attempt to infer semantic segmentation or intention values from its inputs, nor to mimic the output behavior of the teacher. It instead attempts to capture the representation of the teacher inputs that are relevant for driving. Our training does not require laborious annotations such as maps or objects in three dimensions; even the teacher model just requires two-dimensional segmentation and intention values. Moreover, our model runs in real time of 59 FPS. We test our approach on recent simulated and real-world driving datasets, and introduce a more challenging but realistic evaluation protocol that considers a run that reaches the destination successful only if it does not violate common traffic rules.

Includes members:
  Member "exploring the limitations of behavior cloning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies")
  Member "learning by cheating":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end model free reinforcement learning for urban driving using implicit affordances")
  Member "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "cirl controllable imitative reinforcement learning for vision based self driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "learning to steer by mimicking features from heterogeneous auxiliary networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hearing lips improving lip reading by distilling speech recognizers")
  Member "chauffeurnet learning to drive by imitating the best and synthesizing the worst":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "virtual to real reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)