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: "Learning Distributions from Random Walks"
  Abstract: We introduce a new model of distributions generated by random walks on graphs. This model suggests a variety of learning problems, using the definitions and models of distribution learning defined in [6]. Our framework is general enough to model previously studied distribution learning problems, as well as to suggest new applications. We describe special cases of the general problem, and investigate their relative difficulty. We present algorithms to solve the learning problem under various conditions.

Connected through hyperedge HE-1 (induced by source paper "Applying Winnow to Context-Sensitive Spelling Correction"):
  with members:
    - "Active Learning with Committees for Text Categorization"
    - "Mistake-Driven Learning in Text Categorization"

Connected through hyperedge HE-2 (induced by source paper "Efficient Algorithms for Inverting Evolution"):
  with members:
    - "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)"
    - "TREE CONTRACTIONS AND EVOLUTIONARY TREES"
    - "Constructing Big Trees from Short Sequences"
    - "Design of Optimization Criteria for Multiple Sequence Alignment"

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