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 "latent space reinforcement learning for steering angle prediction"):
  Abstract: Model-free reinforcement learning has recently been shown to successfully learn navigation policies from raw sensor data. In this work, we address the problem of learning driving policies for an autonomous agent in a high-fidelity simulator. Building upon recent research that applies deep reinforcement learning to navigation problems, we present a modular deep reinforcement learning approach to predict the steering angle of the car from raw images. The first module extracts a low-dimensional latent semantic representation of the image. The control module trained with reinforcement learning takes the latent vector as input to predict the correct steering angle. The experimental results have showed that our method is capable of learning to maneuver the car without any human control signals.

Includes members:
  Member "autonomous driving in reality with reinforcement learning and image translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "modular vehicle control for transferring semantic information to unseen weather conditions using gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards generalizing sensorimotor control across weather conditions")
  Member "virtual to real reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "end to end learning for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "autoencoding beyond pixels using a learned similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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.LG(Machine Learning)