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 "Metrics for Temporal Difference Learning"):
  Abstract: For an absorbing Markov chain with a reinforcement on each transition, Bertsekas (1995a) gives a simple example where the function learned by TD( ll ) depends on ll . Bertsekas showed that for ll =1 the approximation is optimal with respect to a least-squares error of the value function, and that for ll =0 the approximation obtained by the TD method is poor with respect to the same metric. With respect to the error in the values, TD(1) approximates the function better than TD(0). However, with respect to the error in the differences in the values, TD(0) approximates the function better than TD(1). TD(1) is only better than TD(0) with respect to the former metric rather than the latter. In addition, direct TD( ll ) weights the errors unequally, while residual gradient methods (Baird, 1995, Harmon, Baird, & Klopf, 1995) weight the errors equally. For the case of control, a simple Markov decision process is presented for which direct TD(0) and residual gradient TD(0) both learn the optimal policy, while TD( 11 ) learns a suboptimal policy. These results suggest that, for this example, the differences in state values are more significant than the state values themselves, so TD(0) is preferable to TD(1).

Includes members:
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "MultiPlayer Residual Advantage Learning With General Function Approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Spurious Solutions to the Bellman Equation")

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: Reinforcement_Learning