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 "LEARNING LINEAR, SPARSE, FACTORIAL CODES"):
  Abstract: In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially localized, oriented, and bandpass (i.e., wavelet-like). This note shows how the algorithm may be interpreted within a maximum-likelihood framework. Several useful insights emerge from this connection: it makes explicit the relation to statistical independence (i.e., factorial coding), it shows a formal relationship to the algorithm of Bell and Sejnowski (1995), and it suggests how to adapt parameters that were previously fixed. This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology. This research is sponsored by an Individual National Research Service Award to B.A.O. (NIMH F32-MH11062) and by a grant from the National Science Foundation under contract ASC-9217041 (this award includes funds from ARPA provided under the HPCC program) to CBCL.

Includes members:
  Member "A New Learning Algorithm for Blind Signal Separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules")
  Member "Analyzing Hyperspectral Data with Independent Component Analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")

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: Neural_Networks