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: "Locally Connected Recurrent Networks"
  Abstract: Lai-Wan CHAN and Evan Fung-Yu YOUNG Computer Science Department, The Chinese University of Hong Kong New Territories, Hong Kong Email : lwchan@cs.cuhk.hk Technical Report : CS-TR-95-10 Abstract The fully connected recurrent network (FRN) using the on-line training method, Real Time Recurrent Learning (RTRL), is computationally expensive. It has a computational complexity of O(N 4 ) and storage complexity of O(N 3 ), where N is the number of non-input units. We have devised a locally connected recurrent model which has a much lower complexity in both computational time and storage space. The ring-structure recurrent network (RRN), the simplest kind of the locally connected has the corresponding complexity of O(mn+np) and O(np) respectively, where p, n and m are the number of input, hidden and output units respectively. We compare the performance between RRN and FRN in sequence recognition and time series prediction. We tested the networks' ability in temporal memorizing power and time warpping ability in the sequence recognition task. In the time series prediction task, we used both networks to train and predict three series; a periodic series with white noise, a deterministic chaotic series and the sunspots data. Both tasks show that RRN needs a much shorter training time and the performance of RRN is comparable to that of FRN.

Connected through hyperedge HE-1 (induced by source paper "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"):
  with members:
    - "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"
    - "Neural net architectures for temporal sequence processing"
    - "A `SELF-REFERENTIAL' WEIGHT MATRIX"

Connected through hyperedge HE-2 (induced by source paper "A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

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