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 "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"):
  Abstract: This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local model - ing approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its pre diction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements dur ing explo - ration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real - time learning of the task within 40 to 100 trials. * Address of both authors: Massachusetts Institute of Technology, The Artificial Intelligence Laboratory & The Department of Brain and Cognitive Sciences, 545 Technology Square, Cambride, MA 02139, USA. Email: ss-chaal@ai.mit.edu, cga@ai.mit.edu. Support was provided by the Air Force Office of Sci entific Research and by Siemens Cor pora tion. Support for the first author was provided by the Ger man Scholar ship Foundation and the Alexander von Hum boldt Founda tion. Support for the second author was provided by a Na tional Sci ence Foundation Pre sidential Young Investigator Award. We thank Gideon Stein for im ple ment ing the first version of LWR on the i860 microprocessor, and Gerrie van Zyl for build ing the devil stick robot and implementing the first version of devil stick learning.

Includes members:
  Member "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Induction of Multiscale Temporal Structure")
  Member "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "In  Active Learning with Statistical Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Information-based objective functions for active data selection")
  Member "Forward models: Supervised learning with a distal teacher":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Comparison of Action Selection Learning Methods")
  Member "Efficient Locally Weighted Polynomial Regression Predictions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "On the Greediness of Feature Selection Algorithms")
  Member "Reinforcement Learning in the Multi-Robot Domain":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Locally Weighted Learning for Control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Issues in Using Function Approximation for Reinforcement Learning")
  Member "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic")

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: Case_Based