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: "A Methodology for Processing Problem Constraints in Genetic Programming"
  Abstract: Search mechanisms of artificial intelligence combine two elements: representation, which determines the search space, and a search mechanism, which actually explores the space. Unfortunately, many searches may explore redundant and/or invalid solutions. Genetic programming refers to a class of evolutionary algorithms based on genetic algorithms but utilizing a parameterized representation in the form of trees. These algorithms perform searches based on simulation of nature. They face the same problems of redundant/invalid subspaces. These problems have just recently been addressed in a systematic manner. This paper presents a methodology devised for the public domain genetic programming tool lil-gp. This methodology uses data typing and semantic information to constrain the representation space so that only valid, and possibly unique, solutions will be explored. The user enters problem-specific constraints, which are transformed into a normal set. This set is checked for feasibility, and subsequently it is used to limit the space being explored. The constraints can determine valid, possibly unique space. Moreover, they can also be used to exclude subspaces the user considers uninteresting, using some problem-specific knowledge. A simple example is followed thoroughly to illustrate the constraint language, transformations, and the normal set. Experiments with boolean 11-multiplexer illustrate practical applications of the method to limit redundant space exploration by utilizing problem-specific knowledge. fl Supported by a grant from NASA/JSC: NAG 9-847.

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "Strongly Typed Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "A Simulation of Adaptive Agents in a Hostile Environment"
    - "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"
    - "Type Inheritance in Strongly Typed Genetic Programming"
    - "Modeling Distributed Search via Social Insects"
    - "1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++"
    - "Program Optimization for Faster Genetic Programming"

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: Genetic_Algorithms