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: "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"
  Abstract: This paper sets out a conceptual framework for the open-ended artificial evolution of complex behaviour in autonomous agents. If recurrent dynamical neural networks (or similar) are used as phenotypes, then a Genetic Algorithm that employs variable length genotypes, such as Inman Harvey's SAGA, is capable of evolving arbitrary levels of be-havioural complexity. Furthermore, with simple restrictions on the encoding scheme that governs how genotypes develop into phenotypes, it may be guaranteed that if an increase in fitness requires an increase in be-havioural complexity, then it will evolve. In order for this process to be practicable as a design alternative, however, the time periods involved must be acceptable. The final part of this paper looks at general ways in which the encoding scheme may be modified to speed up the process. Experiments are reported in which different categories of scheme were tested against each other, and conclusions are offered as to the most promising type of encoding scheme for a vi able open-ended Evolutionary Robotics.

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 "POWER OF NEURAL NETS"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"
    - "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Challenges in Evolving Controllers for Physical Robots"):
  with members:
    - "of a simulator for evolving morphology are: Universal the simulator should cover an infinite gen"

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