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: "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
  Abstract: Numerous recent papers focus on standard recurrent nets' problems with long time lags between relevant signals. Some propose rather sophisticated, alternative methods. We show: many problems used to test previous methods can be solved more quickly by random weight guessing.

Connected through hyperedge HE-1 (induced by source paper "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"):
  with members:
    - "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "A Computer Scientist's View of Life, the Universe, and Everything"

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:
    - "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"
    - "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 "LONG SHORT-TERM MEMORY Neural Computation 9(8):1735-1780, 1997"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"):
  with members:
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Diffusion of Context and Credit Information in Markovian Models"
    - "Boltzmann Chains and Hidden Markov Models"

Connected through hyperedge HE-5 (induced by source paper "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "A Practical Bayesian Framework for Backprop Networks"

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