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: "Properties of Genetic Representations of Neural Architectures"
  Abstract: Genetic algorithms and related evolutionary techniques offer a promising approach for automatically exploring the design space of neural architectures for artificial intelligence and cognitive modeling. Central to this process of evolutionary design of neural architectures (EDNA) is the choice of the representation scheme that is used to encode a neural architecture in the form of a gene string (genotype) and to decode a genotype into the corresponding neural architecture (phenotype). The representation scheme used not only constrains the class of neural architectures that are representable (evolvable) in the system, but also determines the efficiency and the time-space complexity of the evolutionary design procedure as a whole. This paper identifies and discusses a set of properties that can be used to characterize different representations used in EDNA and to design or select representations with the necessary properties for particular classes of applications.

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 "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"):
  with members:
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Generative Learning Structures and Processes for Generalized Connectionist Networks"):
  with members:
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Pruning Strategies for the MTiling Constructive Learning Algorithm"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "Faster Learning in Multi-Layer Networks by Handling"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Analysis of Neurocontrollers Designed by Simulated Evolution"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Issues in Evolutionary Robotics"
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"

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