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: "of a simulator for evolving morphology are: Universal the simulator should cover an infinite gen"
  Abstract: Funes, P. and Pollack, J. (1997) Computer Evolution of Buildable Objects. Fourth European Conference on Artificial Life. P. Husbands and I. Harvey, eds., MIT Press. pp 358-367. knowledge into the program, which would result in familiar structures, we provided the algorithm with a model of the physical reality and a purely utilitarian fitness function, thus supplying measures of feasibility and functionality. In this way the evolutionary process runs in an environment that has not been unnecessarily constrained. We added, however, a requirement of computability to reject overly complex structures when they took too long for our simulations to evaluate. The results are encouraging. The evolved structures had a surprisingly alien look: they are not based in common knowledge on how to build with brick toys; instead, the computer found ways of its own through the evolutionary search process. We were able to assemble the final designs manually and confirm that they accomplish the objectives introduced with our fitness functions. After some background on related problems, we describe our physical simulation model for two-dimensional Lego structures, and the representation for encoding them and applying evolution. We demonstrate the feasibility of our work with photos of actual objects which were the result of particular optimizations. Finally, we discuss future work and draw some conclusions. In order to evolve both the morphology and behavior of autonomous mechanical devices which can be manufactured, one must have a simulator which operates under several constraints, and a resultant controller which is adaptive enough to cover the gap between simulated and real world. eral space of mechanisms. Conservative - because simulation is never perfect, it should preserve a margin of safety. Efficient - it should be quicker to test in simulation than through physical production and test. Buildable - results should be convertible from a simula tion to a real object Computer Evolution of Buildable Objects Abstract The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has been constrained by the reality gap which implies that resultant objects are usually not buildable. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of parts. Evolution takes place in a simulator we designed, which computes forces and stresses and predicts failure for 2-dimensional Lego structures. The final printout of our program is a schematic assembly, which can then be built physically. We demonstrate its functionality in several different evolved entities.

Connected through hyperedge HE-1 (induced by source paper "A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Issues in Evolutionary Robotics"
    - "Neural Networks in an Artificial Life Perspective"

Connected through hyperedge HE-2 (induced by source paper "Challenges in Evolving Controllers for Physical Robots"):
  with members:
    - "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"

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