human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Hardness of Learning Halfspaces with Noise"
  Abstract: Learning an unknown halfspace (also called a perceptron) from labeled examples is one of the classic problems in machine learning. In the noise-free case, when a halfspace consistent with all the training examples exists, the problem can be solved in polynomial time using linear programming. However, under the promise that a halfspace consistent with a fraction $(1-\varepsilon)$ of the examples exists (for some small constant $\varepsilon>0$), it was not known how to efficiently find a halfspace that is correct on even 51% of the examples. Nor was a hardness result that ruled out getting agreement on more than 99.9% of the examples known. In this work, we close this gap in our understanding and prove that even a tiny amount of worst-case noise makes the problem of learning halfspaces intractable in a strong sense. Specifically, for arbitrary $\epsilon,\delta > 0$, we prove that given a set of examples-label pairs from the hypercube, a fraction $(1-\varepsilon)$ of which can be explained by a halfspace, it is NP-hard to find a halfspace that correctly labels a fraction $(1/2+\delta)$ of the examples. The hardness result is tight since it is trivial to get agreement on $1/2$ the examples. In learning theory parlance, we prove that weak proper agnostic learning of halfspaces is hard. This settles a question that was raised by Blum et al., in their work on learning halfspaces in the presence of random classification noise [Algorithmica, 22 (1998), pp. 35-52], and raised by authors of some more recent works as well. Along the way, we also obtain a strong hardness result for another basic computational problem: solving a linear system over the rationals.

Connected through hyperedge HE-1 (induced by source paper "The Complexity of Improperly Learning Large Margin Halfspaces."):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Learning noisy perceptrons by a perceptron in polynomial time"):
  with members:
    - "A polynomial-time algorithm for learning noisy linear threshold functions"

Connected through hyperedge HE-3 (induced by source paper "A polynomial-time algorithm for learning noisy linear threshold functions"):
  with members:
    - "Learning noisy perceptrons by a perceptron in polynomial time"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics