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 "monocular plan view networks for autonomous driving"):
  Abstract: Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth. We propose a simple transformation of observations into a bird's eye view, also known as plan view, for end-to-end control. We detect vehicles and pedestrians in the first person view and project them into an overhead plan view. This representation provides an abstraction of the environment from which a deep network can easily deduce the positions and directions of entities. Additionally, the plan view enables us to leverage advances in 3D object detection in conjunction with deep policy learning. We evaluate our monocular plan view network on the photo-realistic Grand Theft Auto V simulator. A network using both a plan view and front view causes less than half as many collisions as previous detection-based methods and an order of magnitude fewer collisions than pure pixel-based policies.

Includes members:
  Member "pixor real time 3d object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "end to end learning of multi sensor 3d tracking by detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "conditional affordance learning for driving in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "learning to move with affordance maps")
  Member "deep object centric policies for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "on offline evaluation of vision based driving models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of deep learning applications to autonomous vehicle control")
  Member "interpretable learning for self driving cars by visualizing causal attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretable end to end urban autonomous driving with latent deep reinforcement learning")
  Member "deep object centric representations for generalizable robot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")

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)