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: "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles"
  Abstract: According to Wolpert's no-free-lunch (NFL) theorems [1, 2], gener-alisation in the absence of domain knowledge is necessarily a zero-sum enterprise. Good generalisation performance in one situation is always offset by bad performance in another. Wolpert notes that the theorems do not demonstrate that effective generalisation is a logical impossibility but merely that a learner's bias (or assumption set) is of key importance

Connected through hyperedge HE-1 (induced by source paper "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"):
  with members:
    - "Simple Genetic Programming for Supervised Learning Problems"
    - "Measuring the Difficulty of Specific Learning Problems"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Truth-from-Trash Learning and the Mobot"
    - "Statistical Biases in Backpropagation Learning  Keywords: Cognitive Science, Pattern recognition"

Connected through hyperedge HE-2 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"

Connected through hyperedge HE-3 (induced by source paper "Separability is a Learner's Best Friend"):
  with members:
    - "Parity: The Problem that Won't Go Away"

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