human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
  Abstract: This paper introduces the "incremental self-improvement paradigm". Unlike previous methods, incremental self-improvement encourages a reinforcement learning system to improve the way it learns, and to improve the way it improves the way it learns ..., without significant theoretical limitations | the system is able to "shift its inductive bias" in a universal way. Its major features are: (1) There is no explicit difference between "learning", "meta-learning", and other kinds of information processing. Using a Turing machine equivalent programming language, the system itself occasionally executes self-delimiting, initially highly random "self-modification programs" which modify the context-dependent probabilities of future action sequences (including future self-modification programs). (2) The system keeps only those probability modifications computed by "useful" self-modification programs: those which bring about more payoff (reward, reinforcement) per time than all previous self-modification programs. (3) The computation of payoff per time takes into account all the computation time required for learning | the entire system life is considered: boundaries between learning trials are ignored (if there are any). A particular implementation based on the novel paradigm is presented. It is designed to exploit what conventional digital machines are good at: fast storage addressing, arithmetic operations etc. Experiments illustrate the system's mode of operation. Keywords: Self-improvement, self-reference, introspection, machine-learning, reinforcement learning. Note: This is the revised and extended version of an earlier report from November 24, 1994.

Connected through hyperedge HE-1 (induced by source paper "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"):
  with members:
    - "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "A Computer Scientist's View of Life, the Universe, and Everything"
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"

Connected through hyperedge HE-2 (induced by source paper "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "Two Methods for Hierarchy Learning in Reinforcement Environments"

Connected through hyperedge HE-3 (induced by source paper "Two Methods for Hierarchy Learning in Reinforcement Environments"):
  with members:
    - "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."

Connected through hyperedge HE-4 (induced by source paper "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "A Practical Bayesian Framework for Backprop Networks"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Reinforcement_Learning