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: "Toward Efficient Agnostic Learning"
  Abstract: In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.

Connected through hyperedge HE-1 (induced by source paper "Cognitive Computation (Extended Abstract)"):
  with members:
    - "Learning to Reason with a Restricted View"

Connected through hyperedge HE-2 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning in the Presence of Malicious Errors"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Learning Switching Concepts"

Connected through hyperedge HE-3 (induced by source paper "Rationality and Intelligence"):
  with members:
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "A Formalization of Explanation-Based Macro-operator Learning"

Connected through hyperedge HE-4 (induced by source paper "Learning in the Presence of Malicious Errors"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Statistical Queries and Faulty PAC Oracles"
    - "On Learning Visual Concepts and DNF Formulae"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Pac Learning, Noise, and Geometry"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "On Learning Conjunctions with Malicious Noise"

Connected through hyperedge HE-5 (induced by source paper "PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)"):
  with members:
    - "Noise-Tolerant Parallel Learning of Geometric Concepts"
    - "Learning With Unreliable Boundary Queries"

Connected through hyperedge HE-6 (induced by source paper "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"

Connected through hyperedge HE-7 (induced by source paper "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"):
  with members:
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
    - "Preventing "Overfitting" of Cross-Validation Data"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"

Connected through hyperedge HE-8 (induced by source paper "On the Complexity of Function Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Simulating Access to Hidden Information while Learning"
    - "Improved Bounds about On-line Learning of Smooth Functions of a Single Variable"

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: Theory