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 "Rigorous Learning Curve Bounds from Statistical Mechanics"):
  Abstract: In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over the well-established Vapnik-Chervonenkis theory is that our bounds can be considerably tighter in many cases, and are also more reflective of the true behavior (functional form) of learning curves. This behavior can often exhibit dramatic properties such as phase transitions, as well as power law asymptotics not explained by the VC theory. The disadvantages of our theory are that its application requires knowledge of the input distribution, and it is limited so far to finite cardinality function classes. We illustrate our results with many concrete examples of learning curve bounds derived from our theory.

Includes members:
  Member "Statistical Tests for Comparing Supervised Classification Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping")
  Member "Self bounding learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Pessimistic decision tree pruning based on tree size")
  Member "Markov Decision Processes in Large State Spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "A General Lower Bound on the Number of Examples Needed for Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Inductive Logic Programming")
  Member "Where Genetic Algorithms Excel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")
  Member "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")

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: Theory