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: "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
  Abstract: Robust, flexible and sufficiently general vision systems such as those for recognition and description of complex 3-dimensional objects require an adequate armamentarium of representations and learning mechanisms. This paper briefly analyzes the strengths and weaknesses of different learning paradigms such as symbol processing systems, connectionist networks, and statistical and syntactic pattern recognition systems as possible candidates for providing such capabilities and points out several promising directions for integrating multiple such paradigms in a synergistic fashion towards that goal.

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Forward models: Supervised learning with a distal teacher"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "Refining PID Controllers using Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Generative Learning Structures and Processes for Generalized Connectionist Networks"):
  with members:
    - "Properties of Genetic Representations of Neural Architectures"
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Pruning Strategies for the MTiling Constructive Learning Algorithm"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "Faster Learning in Multi-Layer Networks by Handling"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"):
  with members:
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"

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