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: "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.

Connected through hyperedge HE-1 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"):
  with members:
    - "Improving Regressors using Boosting Techniques"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A model f w depending on a set of parameters w is used to estimate"
    - "Preventing "Overfitting" of Cross-Validation Data"

Connected through hyperedge HE-2 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Sequential PAC Learning"
    - "Sifting informative examples from a random source."
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Inductive Logic Programming"
    - "Pac Learning, Noise, and Geometry"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-3 (induced by source paper "Self bounding learning algorithms"):
  with members:
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Pessimistic decision tree pruning based on tree size"

Connected through hyperedge HE-4 (induced by source paper "Where Genetic Algorithms Excel"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Computational View of Population Genetics (preliminary version)"
    - "MIMIC: Finding Optima by Estimating Probability Densities"

Connected through hyperedge HE-5 (induced by source paper "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Markov Decision Processes in Large State Spaces"
    - "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"
    - "Near-Optimal Performance for Reinforcement Learning in Polynomial Time"

Connected through hyperedge HE-6 (induced by source paper "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
    - "Preventing "Overfitting" of Cross-Validation Data"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"

Connected through hyperedge HE-7 (induced by source paper "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-8 (induced by source paper "Statistical Tests for Comparing Supervised Classification Learning Algorithms"):
  with members:
    - "Back Propagation is Sensitive to Initial Conditions"
    - "Pessimistic decision tree pruning based on tree size"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

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: Theory