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: "Strongly Typed Genetic Programming"
  Abstract: BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming (STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manipulation and list manipulation: (1) the multi-dimensional least-squares regression problem, (2) the multi-dimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"):
  with members:
    - "Duplication of Coding Segments in Genetic Programming"
    - "Type Inheritance in Strongly Typed Genetic Programming"

Connected through hyperedge HE-2 (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-3 (induced by source paper "Type Inheritance in Strongly Typed Genetic Programming"):
  with members:
    - "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"
    - "Modeling Distributed Search via Social Insects"
    - "Entailment for Specification Refinement"
    - "Augmenting Collective Adaptation with Simple Process Agents"
    - "ABSTRACT"
    - "Clique Detection via Genetic Programming  Topics in Combinatorial Optimization"

Connected through hyperedge HE-4 (induced by source paper "Evolving Behavioral Strategies in Predators and Prey"):
  with members:
    - "Evolving Cooperation Strategies"

Connected through hyperedge HE-5 (induced by source paper "A Simulation of Adaptive Agents in a Hostile Environment"):
  with members:
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Evolving Cooperation Strategies"

Connected through hyperedge HE-6 (induced by source paper "A Methodology for Processing Problem Constraints in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

Connected through hyperedge HE-7 (induced by source paper "Towards Automatic Discovery of Building Blocks in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Causality in Genetic Programming"

Connected through hyperedge HE-8 (induced by source paper "1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++"):
  with members:
    - "Evolving Edge Detectors with Genetic Programming edge detectors for 1-D signals and image profiles. The"

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