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 Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"
  Abstract: In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky 1991). Basically, those techniques iteratively sample the search space with respect to a probability distribution which is updated according to the result of previous samples and some predefined strategy. Genetic Algorithms (GAs) (Goldberg 1989) or Greedy Randomized Adaptive Search Procedures (GRASP) (Feo & Resende 1995) are two particular instances of this paradigm. In this paper, we present SAGE, a search algorithm based on the same fundamental mechanisms as those techniques. However, it addresses a class of problems for which it is difficult to design transformation operators to perform local search because of intrinsic constraints in the definition of the problem itself. For those problems, a procedural approach is the natural way to construct solutions, resulting in a state space represented as a tree or a DAG. The aim of this paper is to describe the underlying heuristics used by SAGE to address problems belonging to that class. The performance of SAGE is analyzed on the problem of grammar induction and its successful application to problems from the recent Abbadingo DFA learning competition is presented.

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 "New Evidence Driven State Merging Algorithm"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "A Stochastic Search Approach to Grammar Induction"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Survey of Evolution Strategies"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"
    - "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"

Connected through hyperedge HE-4 (induced by source paper "A Survey of Evolution Strategies"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On The State of Evolutionary Computation"
    - "A Stochastic Search Approach to Grammar Induction"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Evaluating Evolutionary Algorithms"
    - "Optimal Mutation Rates in Genetic Search"
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"

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