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: "Modeling the Evolution of Motivation"
  Abstract: In order for learning to improve the adaptiveness of an animal's behavior and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For example, many circumstances that damage an animal, or otherwise reduce its fitness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the fitness consequences of its actions as a "motivation system," and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of different architectures for generating action and learning, in worlds of differing complexity. We find that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the fitness of the actions the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated.

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 "Generalization and scaling in reinforcement learning"):
  with members:
    - "Adaptation in constant utility non-stationary environments"
    - "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"
    - "Emergent Control and Planning in an Autonomous Vehicle"

Connected through hyperedge HE-3 (induced by source paper "Auto-teaching: networks that develop their own teaching input"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Neural Networks in an Artificial Life Perspective"

Connected through hyperedge HE-4 (induced by source paper "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "The Schema Theorem and Price's Theorem"
    - "Evolutionary Programming and Evolution Strategies: Similarities and Differences"
    - "Genetic Programming with One-Point Crossover and Point Mutation"

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