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: "Radial Basis Functions: L p -approximation orders with scattered centres"
  Abstract: In this paper we generalize several results on uniform approximation orders with radial basis functions in (Buhmann, Dyn and Levin, 1993) and (Dyn and Ron, 1993) to L p -approximation orders. These results apply, in particular, to approximants from spaces spanned by translates of radial basis functions by scattered centres. Examples to which our results apply include quasi-interpolation and least-squares approximation from radial function spaces.

Connected through hyperedge HE-1 (induced by source paper "APPROXIMATION IN L p (R d FROM SPACES SPANNED BY THE PERTURBED INTEGER TRANSLATES OF"):
  with members:
    - "AN UPPER BOUND ON THE APPROXIMATION POWER OF PRINCIPAL SHIFT-INVARIANT SPACES"
    - "Radial basis function approximation: from gridded centers to scattered centers"

Connected through hyperedge HE-2 (induced by source paper "Radial basis function approximation: from gridded centers to scattered centers"):
  with members:
    - "APPROXIMATION IN L p (R d FROM SPACES SPANNED BY THE PERTURBED INTEGER TRANSLATES OF"
    - "Negative observations concerning approximations from spaces generated by scattered shifts of functions vanishing at 1"
    - "Approximation from shift-invariant subspaces of L"

Connected through hyperedge HE-3 (induced by source paper "AN UPPER BOUND ON THE APPROXIMATION POWER OF PRINCIPAL SHIFT-INVARIANT SPACES"):
  with members:
    - "APPROXIMATION IN L p (R d FROM SPACES SPANNED BY THE PERTURBED INTEGER TRANSLATES OF"

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: Neural_Networks