human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "On the Complexity of Function Learning"):
  Abstract: The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range f0; 1g. Much less is known about the theory of learning functions with a larger range such as IN or IR. In particular relatively few results exist about the general structure of common models for function learning, and there are only very few nontrivial function classes for which positive learning results have been exhibited in any of these models. We introduce in this paper the notion of a binary branching adversary tree for function learning, which allows us to give a somewhat surprising equivalent characterization of the optimal learning cost for learning a class of real-valued functions (in terms of a max-min definition which does not involve any "learning" model). Another general structural result of this paper relates the cost for learning a union of function classes to the learning costs for the individual function classes. Furthermore, we exhibit an efficient learning algorithm for learning convex piecewise linear functions from IR d into IR. Previously, the class of linear functions from IR d into IR was the only class of functions with multi-dimensional domain that was known to be learnable within the rigorous framework of a formal model for on-line learning. Finally we give a sufficient condition for an arbitrary class F of functions from IR into IR that allows us to learn the class of all functions that can be written as the pointwise maximum of k functions from F . This allows us to exhibit a number of further nontrivial classes of functions from IR into IR for which there exist efficient learning algorithms.

Includes members:
  Member "How to Use Expert Advice (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks")
  Member "Simulating Access to Hidden Information while Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Toward Efficient Agnostic Learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Cognitive Computation (Extended Abstract)")
  Member "Improved Bounds about On-line Learning of Smooth Functions of a Single Variable":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ON-LINE LEARNING OF LINEAR FUNCTIONS")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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