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: "Parity: The Problem that Won't Go Away"
  Abstract: It is well-known that certain learning methods (e.g., the perceptron learning algorithm) cannot acquire complete, parity mappings. But it is often overlooked that state-of-the-art learning methods such as C4.5 and backpropagation cannot generalise from incomplete parity mappings. The failure of such methods to generalise on parity mappings may be sometimes dismissed on the grounds that it is `impossible' to generalise over such mappings, or that parity problems are mathematical constructs having little to do with real-world learning. However, this paper argues that such a dismissal is unwarranted. It shows that parity mappings are hard to learn because they are statistically neutral and that statistical neutrality is a property which we should expect to encounter frequently in real-world contexts. It also shows that the generalization failure on parity mappings occurs even when large, minimally incomplete mappings are used for training purposes, i.e., when claims about the impossibility of generalization are particularly suspect.

Connected through hyperedge HE-1 (induced by source paper "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents:"
    - "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Truth-from-Trash Learning and the Mobot"):
  with members:
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

Connected through hyperedge HE-3 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"):
  with members:
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Constructing Nominal Xof-N Attributes"
    - "Extending Theory Refinement to M-of-N Rules"
    - "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"

Connected through hyperedge HE-4 (induced by source paper "Separability is a Learner's Best Friend"):
  with members:
    - "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles"

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