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: "Observations on Cortical Mechanisms for Object Recognition and Learning"
  Abstract: This paper sketches several aspects of a hypothetical cortical architecture for visual object recognition, based on a recent computational model. The scheme relies on modules for learning from examples, such as Hyperbf-like networks, as its basic components. Such models are not intended to be precise theories of the biological circuitry but rather to capture a class of explanations we call Memory-Based Models (MBM) that contains sparse population coding, memory-based recognition and codebooks of prototypes. Unlike the sigmoidal units of some artificial neural networks, the units of MBMs are consistent with the usual description of cortical neurons as tuned to multidimensional optimal stimuli. We will describe how an example of MBM may be realized in terms of cortical circuitry and biophysical mechanisms, consistent with psychophysical and physiological data. A number of predictions, testable with physiological techniques, are made. This memo describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by grants from the Office of Naval Research under contracts N00014-92-J-1879 and N00014-93-1-0385; and by a grant from the National Science Foundation under contract ASC-9217041 (this award includes funds from ARPA provided under the HPCC program). Additional support is provided by the North Atlantic Treaty Organization, ATR Audio and Visual Perception Research Laboratories, Mitsubishi Electric Corporation, Sumitomo Metal Industries, and Siemens AG. Support for the A.I. Laboratory's artificial intelligence research is provided by ARPA contract N00014-91-J-4038. Tomaso Poggio is supported by the Uncas and Helen Whitaker Chair at MIT's Whitaker College.

Connected through hyperedge HE-1 (induced by source paper "Learning networks for face analysis and synthesis"):
  with members:
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Learning from incomplete data"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "Regularization Theory and Neural Networks Architectures"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "Efficient Implementation of Gaussian Processes"

Connected through hyperedge HE-2 (induced by source paper "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"):
  with members:
    - "Theory of Synaptic Plasticity in Visual Cortex"
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network"
    - "An integrated approach to the study of object features in visual recognition"
    - "Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features"
    - "3D Object Recognition Using Unsupervised Feature Extraction"
    - "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"
    - "Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural 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