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: "Case-Based Probability Factoring in Bayesian Belief Networks"
  Abstract: Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported.

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 "Decision-Theoretic Case-Based Reasoning"):
  with members:
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"

Connected through hyperedge HE-3 (induced by source paper "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"):
  with members:
    - "Some Experiments with Real-time Decision Algorithms"
    - "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks"
    - "Algebraic Techniques for Efficient Inference in Bayesian Networks"
    - "Interpretation of Complex Scenes Using Bayesian Networks"

Connected through hyperedge HE-4 (induced by source paper "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Generation Gaps Revisited"
    - "A Genetic Algorithm for File and Task Placement in a Distributed System"
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
    - "Genetic algorithms with multi-parent recombination"
    - "Heuristic for Improved Genetic Bin Packing"
    - "A PARALLEL ISLAND MODEL GENETIC ALGORITHM FOR THE MULTIPROCESSOR SCHEDULING PROBLEM"
    - "A GENETIC ALGORITHM FOR FRAGMENT ALLOCATION IN A DISTRIBUTED DATABASE SYSTEM"

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