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: "Truth-from-Trash Learning and the Mobot"
  Abstract: As natural resources become less abundant, we naturally become more interested in, and more adept at utilisation of waste materials. In doing this we are bringing to bear a ploy which is of key importance in learning | or so I argue in this paper. In the `Truth from Trash' model, learning is viewed as a process which uses environmental feedback to assemble fortuitous sensory predispositions (sensory `trash') into useful, information vehicles, i.e., `truthful' indicators of salient phenomena. The main aim will be to show how a computer implementation of the model has been used to enhance (through learning) the strategic abilities of a simulated, football playing mobot.

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."
    - "Statistical Biases in Backpropagation Learning  Keywords: Cognitive Science, Pattern recognition"
    - "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles"

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 "Parity: The Problem that Won't Go Away"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Separability is a Learner's Best Friend"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"

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