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: "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"
  Abstract: This paper addresses a class of learning problems that require a construction of descriptions that combine both M-of-N rules and traditional Disjunctive Normal form (DNF) rules. The presented method learns such descriptions, which we call conditional M-of-N rules, using the hypothesis-driven constructive induction approach. In this approach, the representation space is modified according to patterns discovered in the iteratively generated hypotheses. The need for the M-of-N rules is detected by observing "exclusive-or" or "equivalence" patterns in the hypotheses. These patterns indicate symmetry relations among pairs of attributes. Symmetrical attributes are combined into maximal symmetry classes. For each symmetry class, the method constructs a "counting attribute" that adds a new dimension to the representation space. The search for hypothesis in iteratively modified representation spaces is done by the standard AQ inductive rule learning algorithm. It is shown that the proposed method is capable of solving problems that would be very difficult to tackle by any of the traditional symbolic learning methods.

Connected through hyperedge HE-1 (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"
    - "Parity: The Problem that Won't Go Away"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"

Connected through hyperedge HE-2 (induced by source paper "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"):
  with members:
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a"

Connected through hyperedge HE-3 (induced by source paper "Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents:"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

Connected through hyperedge HE-4 (induced by source paper "Parity: The Problem that Won't Go Away"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Truth-from-Trash Learning and the Mobot"
    - "Separability is a Learner's Best Friend"

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: Rule_Learning