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: "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
  Abstract: Many neural net learning algorithms aim at finding "simple" nets to explain training data. The expectation is: the "simpler" the networks, the better the generalization on test data (! Occam's razor). Previous implementations, however, use measures for "simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoff's algorithmic probability. Likewise, most previous approaches (especially those of the "Bayesian" kind) suffer from the problem of choosing appropriate priors. This paper addresses both issues. It first reviews some basic concepts of algorithmic complexity theory relevant to machine learning, and how the Solomonoff-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for finding "algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. For a given problem, solution candidates are computed by efficient "self-sizing" programs that influence their own runtime and storage size. The probabilistic search algorithm finds the "good" programs (the ones quickly computing algorithmically probable solutions fitting the training data). Simulations focus on the task of discovering "algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural net algorithms. Much remains do be done, however, to make large scale applications and "incremental learning" feasible.

Connected through hyperedge HE-1 (induced by source paper "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"):
  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)"
    - "Two Methods for Hierarchy Learning in Reinforcement Environments"

Connected through hyperedge HE-2 (induced by source paper "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"):
  with members:
    - "In  Improving Elevator Performance Using Reinforcement Learning"
    - "Markov games as a framework for multi-agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"):
  with members:
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
    - "LONG SHORT-TERM MEMORY Neural Computation 9(8):1735-1780, 1997"

Connected through hyperedge HE-4 (induced by source paper "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."):
  with members:
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
    - "Two Methods for Hierarchy Learning in Reinforcement Environments"

Connected through hyperedge HE-5 (induced by source paper "A Computer Scientist's View of Life, the Universe, and Everything"):
  with members:
    - "REINFORCEMENT LEARNING WITH SELF-MODIFYING POLICIES"
    - "Machine Learning,  Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement"

Connected through hyperedge HE-6 (induced by source paper "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"):
  with members:
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
    - "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: Neural_Networks