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: "fast recurrent fully convolutional networks for direct perception in autonomous driving"
  Abstract: Deep convolutional neural networks (CNNs) have been shown to perform extremely well at a variety of tasks including subtasks of autonomous driving such as image segmentation and object classification. However, networks designed for these tasks typically require vast quantities of training data and long training periods to converge. We investigate the design rationale behind end-to-end driving network designs by proposing and comparing three small and computationally inexpensive deep end-to-end neural network models that generate driving control signals directly from input images. In contrast to prior work that segments the autonomous driving task, our models take on a novel approach to the autonomous driving problem by utilizing deep and thin Fully Convolutional Nets (FCNs) with recurrent neural nets and low parameter counts to tackle a complex end-to-end regression task predicting both steering and acceleration commands. In addition, we include layers optimized for classification to allow the networks to implicitly learn image semantics. We show that the resulting networks use 3x fewer parameters than the most recent comparable end-to-end driving network and 500x fewer parameters than the AlexNet variations and converge both faster and to lower losses while maintaining robustness against overfitting.

Connected through hyperedge HE-1 (induced by source paper "cirl controllable imitative reinforcement learning for vision based self driving"):
  with members:
    - "real to virtual domain unification for end to end autonomous driving"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "deep reinforcement learning that matters"
    - "virtual to real reinforcement learning for autonomous driving"
    - "parameter space noise for exploration"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "carla an open urban driving simulator"
    - "attention aware face hallucination via deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning end to end autonomous driving using guided auxiliary supervision"):
  with members:
    - "deep learning a critical appraisal"
    - "taco learning task decomposition via temporal alignment for control"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "end to end driving via conditional imitation learning"
    - "an overview of multi task learning in deep neural networks"
    - "one shot imitation learning"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "end to end driving simulation via angle branched network"):
  with members:
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "end to end driving via conditional imitation learning"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "end to end learning for self driving cars"
    - "asynchronous methods for deep reinforcement learning"
    - "end to end learning of driving models from large scale video datasets"
    - "continuous control with deep reinforcement learning"

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)