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: "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.

Connected through hyperedge HE-1 (induced by source paper "affect based intrinsic rewards for exploration and learning"):
  with members:
    - "explorations and lessons learned in building an autonomous formula sae car from simulations"
    - "learning exploration policies for navigation"
    - "large scale study of curiosity driven learning"
    - "visceral machines risk aversion in reinforcement learning with intrinsic physiological rewards"
    - "on learning intrinsic rewards for policy gradient methods"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "episodic curiosity through reachability"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

Connected through hyperedge HE-2 (induced by source paper "learning by cheating"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "does computer vision matter for action"
    - "objects as points"
    - "pytorch an imperative style high performance deep learning library"
    - "an algorithmic perspective on imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-3 (induced by source paper "causal confusion in imitation learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "causal inference via kernel deviance measures"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "isolating sources of disentanglement in variational autoencoders"
    - "dart noise injection for robust imitation learning"

Connected through hyperedge HE-4 (induced by source paper "affect based intrinsic rewards for learning general representations"):
  with members:
    - "learning exploration policies for navigation"
    - "large scale study of curiosity driven learning"
    - "visceral machines risk aversion in reinforcement learning with intrinsic physiological rewards"
    - "on learning intrinsic rewards for policy gradient methods"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "episodic curiosity through reachability"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "short term prediction and multi camera fusion on semantic grids"):
  with members:
    - "predicting future instance segmentations by forecasting convolutional features"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "long term occupancy grid prediction using recurrent neural networks"
    - "deep network uncertainty maps for indoor navigation"
    - "predicting deeper into the future of semantic segmentation"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "long term on board prediction of people in traffic scenes under uncertainty"

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.CV(Computer Vision and Pattern Recognition)