human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ON LEARNING HOW TO LEARN LEARNING STRATEGIES")
  Member "FLAT MINIMA Neural Computation 9(1):1-42 (1997)":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "ON LEARNING HOW TO LEARN LEARNING STRATEGIES":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time.")
  Member "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "In  Improving Elevator Performance Using Reinforcement Learning")
  Member "A Computer Scientist's View of Life, the Universe, and Everything":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "REINFORCEMENT LEARNING WITH SELF-MODIFYING POLICIES")
  Member "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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