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: "simulated car racing championship competition software manual"
  Abstract: This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games. It provides an overview of the architecture, the instructions to install the software and to run the simple drivers provided in the package, the description of the sensors and the actuators.

Connected through hyperedge HE-1 (induced by source paper "online meta critic learning for off policy actor critic methods"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "learning surrogate losses"
    - "learning to teach with dynamic loss functions"
    - "meta gradient reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-2 (induced by source paper "end to end driving simulation via angle branched network"):
  with members:
    - "fast recurrent fully convolutional networks for direct perception in autonomous driving"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "parameter sharing reinforcement learning architecture for multi agent driving behaviors"):
  with members:
    - "a deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots"
    - "counterfactual multi agent policy gradients"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "asynchronous methods for deep reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "learning to drive using inverse reinforcement learning and deep q networks"
    - "a centralized reinforcement learning method for multi agent job scheduling in grid"

Connected through hyperedge HE-4 (induced by source paper "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"):
  with members:
    - "carla an open urban driving simulator"
    - "deep reinforcement learning a brief survey"
    - "continuous control with deep reinforcement learning"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"

Connected through hyperedge HE-5 (induced by source paper "vehicle platooning simulations with functional reactive programming"):
  with members:
    - "juniper a functional reactive programming language for the arduino"
    - "formal verification of autonomous vehicle platooning"

Connected through hyperedge HE-6 (induced by source paper "combining deep reinforcement learning and safety based control for autonomous driving"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning in parameterized action space"
    - "massively parallel methods for deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

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.AI(Artificial Intelligence)