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: "Learning networks for face analysis and synthesis"
  Abstract: This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for the problem of face detection, "near miss" faces as well) with descriptive parameters for pose, expression, identity, and face vs. non-face. Then, by using example-based learning techniques, we develop networks for performing analysis tasks such as pose and expression estimation, face recognition, and face detection in cluttered scenes. In addition to these analysis applications, we show how the example-based technique can also be used as a novel method for image synthesis that is for computer graphics.

Connected through hyperedge HE-1 (induced by source paper "Receptive Fields for Vision: from Hyperacuity to Object Recognition"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
    - "Models of perceptual learning in vernier hyperacuity"

Connected through hyperedge HE-2 (induced by source paper "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Regularization Theory and Neural Networks Architectures"
    - "A Practical Monte Carlo Implementation of Bayesian Learning"

Connected through hyperedge HE-3 (induced by source paper "Convergence Rates of Approximation by Translates"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Observations on Cortical Mechanisms for Object Recognition and Learning"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"

Connected through hyperedge HE-5 (induced by source paper "FONN: Combining First Order Logic with Connectionist Learning"):
  with members:
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"
    - "Learning Controllers for Industrial Robots"

Connected through hyperedge HE-6 (induced by source paper "Radial Basis Functions for Process Control"):
  with members:
    - "EMRBF: A Statistical Basis for Using Radial Basis Functions for Process Control"

Connected through hyperedge HE-7 (induced by source paper "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"):
  with members:
    - "Prior Information and Generalized Questions"
    - "Regularization Theory and Neural Networks Architectures"

Connected through hyperedge HE-8 (induced by source paper "Evaluation of Pattern Classifiers for Fingerprint and OCR Applications"):
  with members:
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "Face Recognition: A Hybrid Neural Network Approach"

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