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: "Measuring the Difficulty of Specific Learning Problems"
  Abstract: Existing complexity measures from contemporary learning theory cannot be conveniently applied to specific learning problems (e.g., training sets). Moreover, they are typically non-generic, i.e., they necessitate making assumptions about the way in which the learner will operate. The lack of a satisfactory, generic complexity measure for learning problems poses difficulties for researchers in various areas; the present paper puts forward an idea which may help to alleviate these. It shows that supervised learning problems fall into two, generic, complexity classes only one of which is associated with computational tractability. By determining which class a particular problem belongs to, we can thus effectively evaluate its degree of generic difficulty.

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:
    - "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"
    - "Island Model Genetic Algorithms and Linearly Separable Problems"

Connected through hyperedge HE-2 (induced by source paper "Interpretable Neural Networks with BP-SOM"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Avoiding Overfitting with BP-SOM"

Connected through hyperedge HE-3 (induced by source paper "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"):
  with members:
    - "Simple Genetic Programming for Supervised Learning Problems"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Truth-from-Trash Learning and the Mobot"
    - "Statistical Biases in Backpropagation Learning  Keywords: Cognitive Science, Pattern recognition"
    - "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles"

Connected through hyperedge HE-4 (induced by source paper "Is Transfer Inductive?"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Statistical Biases in Backpropagation Learning  Keywords: Cognitive Science, Pattern recognition"

Connected through hyperedge HE-5 (induced by source paper "Simple Genetic Programming for Supervised Learning Problems"):
  with members:
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"

Connected through hyperedge HE-6 (induced by source paper "Avoiding Overfitting with BP-SOM"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Interpretable Neural Networks with BP-SOM"

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: Theory