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: "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"
  Abstract: The RTRL algorithm for fully recurrent continually running networks (Robinson and Fallside, 1987)(Williams and Zipser, 1989) requires O(n 4 ) computations per time step, where n is the number of non-input units. I describe a method suited for on-line learning which computes exactly the same gradient and requires fixed-size storage of the same order but has an average time complexity 1 per time step of O(n 3 ).

Connected through hyperedge HE-1 (induced by source paper "Locally Connected Recurrent Networks"):
  with members:
    - "A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks"

Connected through hyperedge HE-2 (induced by source paper "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"):
  with members:
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992)"

Connected through hyperedge HE-3 (induced by source paper "Neural net architectures for temporal sequence processing"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "Induction of Multiscale Temporal Structure"
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"

Connected through hyperedge HE-4 (induced by source paper "A `SELF-REFERENTIAL' WEIGHT MATRIX"):
  with members:
    - "LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992)"

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