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: "Noise-Tolerant Parallel Learning of Geometric Concepts"
  Abstract: We present several efficient parallel algorithms for PAC-learning geometric concepts in a constant-dimensional space that are robust even against malicious misclassification noise of any rate less than 1=2. In particular we consider the class of geometric concepts defined by a polynomial number of (d 1)-dimensional hyperplanes against an arbitrary distribution where each hyperplane has a slope from a set of known slopes, and the class of geometric concepts defined by a polynomial number of (d 1)-dimensional hyperplanes (of unrestricted slopes) against a product distribution. Next we define a complexity measure of any set S of (d1)-dimensional surfaces that we call the variant of S and prove that the class of geometric concepts defined by surfaces of polynomial variant can be efficiently learned in parallel under a product distribution (even under malicious misclassifi-cation noise). Finally, we describe how boosting techniques can be used so that our algorithms' de pendence on * and ffi does not depend on d.

Connected through hyperedge HE-1 (induced by source paper "PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "Learning With Unreliable Boundary Queries"

Connected through hyperedge HE-2 (induced by source paper "Learning Boxes in High Dimension"):
  with members:
    - "Learning Unions of Boxes with Membership and Equivalence Queries"

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