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: "automated curriculum learning by rewarding temporally rare events"
  Abstract: Reward shaping allows reinforcement learning (RL) agents to accelerate learning by receiving additional reward signals. However, these signals can be difficult to design manually, especially for complex RL tasks. We propose a simple and general approach that determines the reward of pre-defined events by their rarity alone. Here events become less rewarding as they are experienced more often, which encourages the agent to continually explore new types of events as it learns. The adaptiveness of this reward function results in a form of automated curriculum learning that does not have to be specified by the experimenter. We demonstrate that this Rarity of Events (RoE) approach enables the agent to succeed in challenging VizDoom scenarios without access to the extrinsic reward from the environment. Furthermore, the results demonstrate that RoE learns a more versatile policy that adapts well to critical changes in the environment. Rewarding events based on their rarity could help in many unsolved RL environments that are characterized by sparse extrinsic rewards but a plethora of known event types.

Connected through hyperedge HE-1 (induced by source paper "adapting to unseen environments through explicit representation of context"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "procedural content generation from automatically generating game levels to increasing generality in machine learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "meta learning by the baldwin effect"
    - "learning curriculum policies for reinforcement learning"
    - "lstm a search space odyssey"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "illuminating generalization in deep reinforcement learning through procedural level generation"):
  with members:
    - "deep reinforcement learning for general video game ai"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "a study on overfitting in deep reinforcement learning"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"
    - "teacher student curriculum 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.AI(Artificial Intelligence)