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: "automating vehicles by deep reinforcement learning using task separation with hill climbing"
  Abstract: Within the context of autonomous vehicles, classical model-based control methods suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. These methods include sampling-based algorithms, lattice-based algorithms and algorithms based on model predictive control (MPC). Recently, end-to-end trained deep neural networks were proposed to map camera images directly to steering control. These algorithms, however, a priori dismiss decades of vehicle dynamics modeling experience, which could be leveraged for control design. #R##N#In this paper, a model-based reinforcement learning (RL) method is proposed for the training of feedforward controllers in the context of autonomous driving. Fundamental philosophy is to offline train on arbitrarily sophisticated models, while online cheaply evaluate a feedforward controller, thereby avoiding the need for online optimization. The contributions are, first, the discussion of two closed-loop control architectures, and, second, the proposition of a simple gradient-free algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). Therefore, a) simultaneous training on separate deterministic tasks with the purpose of encoding motion primitives in a neural network, and b) the employment of maximally sparse rewards in combinations with virtual actuator constraints on velocity in setpoint proximity are advocated. For feedforward controller parametrization, both fully connected (FC) and recurrent neural networks (RNNs) are used.

Connected through hyperedge HE-1 (induced by source paper "online sampling in the parameter space of a neural network for gpu accelerated motion planning of autonomous vehicles"):
  with members:
    - "real time trajectory planning for agv in the presence of moving obstacles a first search then optimization approach"
    - "encoding motion primitives for autonomous vehicles using virtual velocity constraints and neural network scheduling"
    - "learning sampling distributions for robot motion planning"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-2 (induced by source paper "elements of effective deep reinforcement learning towards tactical driving decision making"):
  with members:
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "on a formal model of safe and scalable self driving cars"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "encoding motion primitives for autonomous vehicles using virtual velocity constraints and neural network scheduling"):
  with members:
    - "structured control nets for deep reinforcement learning"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "es is more than just a traditional finite difference approximator"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "end to end learning for self driving cars"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)